{"as_of":"2026-08-05T10:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9748a6d98cdfd9e33c20c6e8a65a24b54bb6621d91be5442161d79afcbaa2bce","coverage":[{"denominator":96,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":96,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-09T17:21:37.982077Z","state":"measured"},{"denominator":97,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":97,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T13:38:06.678316Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.02003","snapshot_observed_at":"2026-08-04T13:38:06.678316Z","title":"Cruz Bournazou, Peter Neubauer, Felix Bießmann, and Erik Rodner","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.02157","last_updated":"2026-08-03T12:37:55Z","snapshot_observed_at":"2026-08-05T09:24:18.164164Z","submitted_at":"2026-08-03T12:37:55Z","title":"RamanPFN: learning from Raman spectral structure with a tabular foundation model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T13:38:06.678316Z"},"links":{"cited_paper":"/paper/2605.02003","citing_paper":"/paper/2608.02157"},"observation_digest":"sha256:106b0c07f0d84c7719ffb91cd77361fe16b5e83c0032b03ebb277e17097c514c","observation_id":"a1dca4fa-56e7-4753-8a76-e994fbc5ec30","resolution":{"observed_at":"2026-08-04T13:38:06.678316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2605.02003/citation-record","integrity":"/paper/2605.02003/integrity","json":"/paper/2605.02003/citation-record.json","paper":"/paper/2605.02003"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Vibrational spectroscopy and its future applications in microbiology.Applied Spectroscopy Reviews, 58(2):132–158","venue":null,"work_id":"07c50d45-917c-463d-b54b-1cfcc22e6a95","year":2023},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:2c41d6457ada523eab6a6f857d8c8725d4b4614c4ec7b0f3be693ab581b77781","observation_id":"57851f90-2ca5-4a2b-9c4c-068bb710e1bb","resolution":{"observed_at":"2026-05-26T00:01:24.136495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4cc614d2-1cee-48d0-a535-dc496980a0e3","year":2015},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:f88b45649cc543b138e2dc4fe68007344b62f85619030a5d3933e78a02fa51a3","observation_id":"a1aaa1a6-f8a6-491f-9f13-680447180f43","resolution":{"observed_at":"2026-05-26T00:01:24.179477Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The role of raman spectroscopy in biopharmaceuticals from development to manufacturing.Analytical and Bioanalytical Chemistry, 414(2):969–991","venue":null,"work_id":"e31ef55a-da3f-46d8-ad9f-567eb2a08939","year":2022},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:3387e432173261ae7d90e947462a086332263791c9f50c742962fd41160b3e35","observation_id":"bd76e8f6-98ef-4f81-8ad7-5731e744dfc7","resolution":{"observed_at":"2026-05-26T00:01:24.113871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rapid identification of pathogenic bacteria using raman spectroscopy and deep learning.Nature communications, 10 (1):4927","venue":null,"work_id":"b3928c1a-c8a2-4e04-9894-cde43041265e","year":2019},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:8b846ba298b9c1559a03419c53c9fe54c36319b497161736d71b2d661d1cd326","observation_id":"ab98d9a7-98dd-4fd6-bc97-0888b167b4db","resolution":{"observed_at":"2026-05-26T00:01:24.117110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An integrated computational pipeline for machine learning-driven diagnosis based on raman spectra of saliva samples.Computers in Biology and Medicine, 171:108028","venue":null,"work_id":"2c8e0b1a-5ad0-4b05-a0ad-532bd538aa92","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:f763916c2e9b10a63d9379f4a280e81def7c2b70d0bd93de088bda1a5bd36a57","observation_id":"84ac613a-a383-4b70-8842-d80b60a7d038","resolution":{"observed_at":"2026-05-26T00:01:24.107358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Open-source raman spectra of chemical compounds for active pharmaceutical ingredient development.Scientific Data, 12(1):498","venue":null,"work_id":"e2f20f4e-5d6e-4c4c-9b10-7e83502f7555","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:1810cc555394a492c8bfc3cd8d5789da8dcce0f78904452d52ef1ee15b5ff8ed","observation_id":"ae60f2c4-6164-467d-9ca3-fdc302ec235e","resolution":{"observed_at":"2026-05-26T00:01:24.096698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Inline raman spectroscopy and indirect hard modeling for concentration monitoring of dissociated acid species.Applied spectroscopy, 75(5):506–519","venue":null,"work_id":"38b90a64-2272-4bbe-bd9d-531b8e2631f0","year":2021},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:ef28b125efb5a1fb7894912b968d5226cd3b6001a38c7c4c677c26d10916fdb9","observation_id":"e806dd4c-ca17-4afc-b222-c048f572efef","resolution":{"observed_at":"2026-05-26T00:01:24.103782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep learning for raman spectroscopy: A review.Analytica, 3(3):287–301","venue":null,"work_id":"2802b213-519d-4a19-8558-5fa6848610c0","year":2022},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:c6e77080291646d2e325e4d484616fe8f1cfa3731e1fceec07f9ab0a84086153","observation_id":"91d4e4b0-4311-425e-9540-b490c589d110","resolution":{"observed_at":"2026-05-26T00:01:24.100556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.16791","last_updated":"2025-11-03T18:47:03Z","snapshot_observed_at":"2026-07-06T21:45:06.566500Z","submitted_at":"2025-06-20T07:14:48Z","title":"TabArena: A Living Benchmark for Machine Learning on Tabular Data","version":4},"cited_work":{"arxiv_id":"2506.16791","doi":"10.15585/mmwr.mm6643a2","metadata_source":"pith","pith_arxiv_id":"2506.16791","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"TabArena: A Living Benchmark for Machine Learning on Tabular Data","venue":"cs.LG","work_id":"155b5349-dee8-4870-965c-d54a700a19de","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"cited_paper":"/paper/2506.16791","citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:eeb382c3601fda586fa03a38de776670b0d70343aa6ff6dd9cb31791aeb2dfc4","observation_id":"249b15a9-4e40-45db-b401-9dc26f480361","resolution":{"observed_at":"2026-05-11T16:21:07.977439Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"TALENT: A tabular analytics and learning toolbox.Journal of Machine Learning Research, 26(226):1–16","venue":null,"work_id":"563e60c1-a73c-4e64-8684-fdf20c0c85a2","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:b932ab0bd6a1e8499f48948efe883e8b3c58c95c4864474f6966f3770cdc7c1a","observation_id":"6b25a774-2a41-4345-8f07-e815a428e636","resolution":{"observed_at":"2026-05-26T00:01:24.120547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T05:14:35.886371Z","title":"The ucr time series archive.IEEE/CAA Journal of Automatica Sinica, 6(6):1293–1305","venue":null,"work_id":"c73562be-4e32-4b80-b569-eb19e30d6423","year":2019},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:8c8d6028d1a82525696ebf2846d4f9aa12c536b387b7bd6d8522b9a876950322","observation_id":"d9624bb5-beee-4e09-bef8-b093a3aed22d","resolution":{"observed_at":"2026-05-26T00:01:24.187226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.00075","last_updated":"2018-10-31T19:24:20Z","snapshot_observed_at":"2026-07-06T07:11:52.518341Z","submitted_at":"2018-10-31T19:24:20Z","title":"The UEA multivariate time series classification archive, 2018","version":1},"cited_work":{"arxiv_id":"1811.00075","doi":null,"metadata_source":"pith","pith_arxiv_id":"1811.00075","snapshot_observed_at":"2026-07-10T19:07:35.064680Z","title":"The UEA multivariate time series classification archive","venue":"cs.LG","work_id":"79852b21-480f-48b1-9483-33c25c5001ab","year":2018},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"cited_paper":"/paper/1811.00075","citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:d950d3bf26c54feea4cb3cb91200664bd5cd9fdb0a55411ff453152ae58ae774","observation_id":"a4fb4fc2-b8c2-433b-9f02-14da25d83d53","resolution":{"observed_at":"2026-05-11T16:21:08.004344Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Artificial intelligence-powered raman spectroscopy through open science and fair principles.ACS nano, 19(44):38189–38218","venue":null,"work_id":"80958fe1-c608-4ee8-8554-55c7a1872079","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:ce836b9556f8b7af41cf888d9440b0504d67699d22657dde645195fd224d2470","observation_id":"e243a79e-4c52-4f68-b1b3-cc61c9d35085","resolution":{"observed_at":"2026-05-26T00:01:24.164879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The fair guiding principles for scientific data management and stewardship","venue":null,"work_id":"7620cb0e-070e-4a64-b5a9-5619083216af","year":2016},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:b049b8aba628fe7b2c1d7e2ae249e1dda8c046e536579480842fe7927ab87b9e","observation_id":"41f86617-4b27-471f-bce7-cb1b535adeb6","resolution":{"observed_at":"2026-05-26T00:01:24.172203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41597-019-0138-y","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Scientific Data","work_id":"3277d931-f64a-4fec-8d76-8ad7da0ec97a","year":2019},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:c5348519dc5b86723aac0eac51e41b730f42a31ece53bc928a4245d0b23acbad","observation_id":"f58c9e0f-7453-4050-9d02-1d8dd3698bfe","resolution":{"observed_at":"2026-05-09T21:38:30.534697Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Convolutional neural networks as a tool for raman spectral mineral classification under low signal, dusty mars conditions.Earth and Space Science, 9(10):e2021EA002125","venue":null,"work_id":"148211b3-ba2f-420a-b8a6-dbd13d019e5d","year":2022},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:7e1362d0b8e8c47c5404746541fe5de8c3eeb93ee7008c543e3372d3c58f84f9","observation_id":"54f4e234-257c-4d73-98d5-f9cfe923403d","resolution":{"observed_at":"2026-05-26T00:01:24.183745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41524-023-01055-y","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Schuetzke, N","venue":"npj Computational Materials","work_id":"ba1336d0-dd04-4480-9156-5b2cf41f4276","year":2023},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:f56f0a1b62f016f04fa37535f2eee9801fecd1298973aadca4e3fe4e80503d7c","observation_id":"53d8f7f6-d434-4f8c-99d4-5877bf11cdc8","resolution":{"observed_at":"2026-05-09T21:38:30.536996Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/pnasnexus/pgae268","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification.PNAS Nexus, 3(8): pgae268","venue":"PNAS Nexus","work_id":"c65e0365-8c1b-434f-ac51-76fe6b1cd1f3","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:2452f3f550c30d6f45d1aac4ba0e9010cbf75ae06bd7232a3fe2648c74f96fa8","observation_id":"cb703781-fb8f-4aa0-b5f5-edcf0c354d77","resolution":{"observed_at":"2026-05-09T21:38:30.547055Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ramanspy: An open-source python package for integrative raman spectroscopy data analysis.Analytical chemistry, 96(21):8492–8500","venue":null,"work_id":"99a98a2f-bcf0-4813-9fd0-dbb9b68eda58","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:80aeb42617bfc3c47248d7fb737ded94ab4c3bacbf1c3299ea47d02f3b628350","observation_id":"ff89df4e-e3c2-4b5d-811e-12229ff5278b","resolution":{"observed_at":"2026-05-26T00:01:24.156437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.105548","doi":"10.1016/j.chemolab.2025.105548","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Byrne, and David Pérez-Guaita","venue":"Chemometrics and Intelligent Laboratory Systems","work_id":"35322038-874f-4826-877c-40f57b6a9a7f","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:0c75cd515feec57e8cbb3ad661d83066af40fe31aa5aeac81efb1fd53505dd4d","observation_id":"6d06e099-09ce-4898-afbf-28f5c95505a9","resolution":{"observed_at":"2026-05-09T21:38:30.532382Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep learning for raman spectroscopy: Benchmarking models for upstream bioprocess monitoring.Measurement, page 118884","venue":null,"work_id":"6f79ab56-1124-400f-845c-2e5e4e58c149","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:8789bd4c9318a8e68e603ebbfee834fb9960e850045e8877e6f052d5b2d60f8a","observation_id":"c6d677f2-4b83-402b-ac3f-4af0b964ca6b","resolution":{"observed_at":"2026-05-26T00:01:24.148917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2025.125861","doi":"10.1016/j.saa.2025.125861","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Nicolas Cruz Bournazou","venue":"Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy","work_id":"b169f77c-9745-4fd0-9c54-119e7833c511","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:476a30f54fabb1a6fc3fcfd20989183c0747187cf5f45a34e7cdd0bf0a53271b","observation_id":"b380757e-d6f9-4207-bf49-9ce7617b76e5","resolution":{"observed_at":"2026-05-09T21:38:30.550364Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/jrs.6842","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Lilek et al","venue":"Journal of Raman Spectroscopy","work_id":"947e25ce-5fb6-4ae8-9c21-95a7e9cac1b1","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:2323f961cd9f1e06a60d821edd71943c3f7bd102a754ac5ebd75bb3d582168f4","observation_id":"717fb85d-2b7d-4053-b43d-282cbc270bfe","resolution":{"observed_at":"2026-05-09T21:38:30.544537Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2508.01188","doi":"10.48550/arxiv.2508.01188","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Spectrumworld: Artificial intelligence foundation for spectroscopy","venue":"arXiv (Cornell University)","work_id":"aa574b99-3951-457b-bfb9-b57425308635","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:b8568e3af6b81c014928fcb8140630327bdd18eb6aab5263c14fc914984547fc","observation_id":"7ec04830-cdad-444e-8172-08f04e5f9483","resolution":{"observed_at":"2026-05-11T16:21:07.958681Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep spectral component filtering as a foundation model for spectral analysis demonstrated in metabolic profiling.Nature Machine Intelligence, 7 (5):743–757","venue":null,"work_id":"1e35bdf0-5886-417c-82ca-e2366a878551","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:31cc6504a1e4cac89997ed4eed58ecc973d00d4b854ceed724cf5176d60a0a46","observation_id":"be83c27c-42a7-48f5-9a00-baf744787fe0","resolution":{"observed_at":"2026-05-26T00:01:24.152727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.16107","last_updated":"2026-07-28T06:13:08Z","snapshot_observed_at":"2026-08-03T08:41:40.710716Z","submitted_at":"2026-01-22T16:54:53Z","title":"Benchmarking Deep Learning Models for Raman Spectroscopy Across Open-Source Datasets","version":2},"cited_work":{"arxiv_id":"2601.16107","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.16107","snapshot_observed_at":"2026-07-29T02:24:14.201985Z","title":"Benchmarking deep learning models for raman spec- troscopy across open-source datasets","venue":null,"work_id":"d38ae849-f5d5-499b-bc1a-f879fb67ba2c","year":2026},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"cited_paper":"/paper/2601.16107","citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:ce2d429a839b5a26d6035605f90c881bb515193fc462313ab5c5488e9136f5fd","observation_id":"84ed250d-f4a0-41c2-a406-f158839739a3","resolution":{"observed_at":"2026-07-29T02:24:14.201985Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Raman-enabled predictions of pro- tein content and metabolites in biopharmaceutical saccharomyces cerevisiae fermentations","venue":null,"work_id":"6bb0770c-9e5a-4338-b7b7-4f496a580d65","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:2e3b9ac8bed351d1b4dc234e48004a2f5c04a12f4483bc916f7e23dfd6105d5f","observation_id":"f14bf649-181a-4413-85db-45eac7983a79","resolution":{"observed_at":"2026-05-26T00:01:24.160961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Combining mechanistic modeling and raman spectroscopy for monitoring antibody chromatographic purification.Processes, 7(10): 683","venue":null,"work_id":"000e652e-3273-4a47-92ab-3104a6c22c2c","year":2019},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:4e9bd18c01ba53746a707c23868ddb46b2a546c88f932856a9b4ec50f6b2fef4","observation_id":"06501709-1bb8-4dbd-bb5c-45d72f778e40","resolution":{"observed_at":"2026-05-26T00:01:24.191010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Soft modeling: the basic design and some extensions.Systems under indirect observation, Part II, 2:36–37","venue":null,"work_id":"0709e9d5-5220-4fe8-85e2-1690ca94e661","year":1982},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:17c9659e628909b3b8f02d071948166f25080b25e9b1a8a40cef039c4cc38b9d","observation_id":"f31cc229-3906-4c51-808f-993e1fa91b2e","resolution":{"observed_at":"2026-05-26T00:01:24.141056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Support-vector networks.Machine learning, 20(3): 273–297","venue":null,"work_id":"822d3ab6-ec9d-4236-af57-22ec352c2bda","year":1995},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:55306284eb92caf050e341e896564aaff753afdb5ca1b11f02f707c82332a0c0","observation_id":"03d6e640-c30f-46fc-b727-c4ad15268c14","resolution":{"observed_at":"2026-05-26T00:01:24.131580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T06:36:03.268346Z","title":"Imagenet: A large- scale hierarchical image database","venue":null,"work_id":"6ee29c54-0013-4f3e-8bcb-5c870412e0cb","year":2009},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:b88ba984ab3d5734dd6b35dcef16d286db4a3ffc752fcd13ccea6cc588945a63","observation_id":"e9f0cac5-c040-43ab-b0fe-1a545d3a4eed","resolution":{"observed_at":"2026-05-26T00:01:24.145335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T09:37:01.212698Z","title":"Glue: A multi-task benchmark and analysis platform for natural language understanding","venue":null,"work_id":"189206c3-17de-4465-90b9-8d62ebd0e8df","year":2018},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:1db92403ac6bcdbe72cc5f4a5b5d204376555a6e548f397b068d7411148bea8f","observation_id":"983d4ff1-45a6-4c4c-bdee-0cb5218725c9","resolution":{"observed_at":"2026-05-26T00:01:24.176115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Why do tree-based models still outperform deep learning on typical tabular data?Advances in neural information processing systems, 35:507–520","venue":null,"work_id":"c09c81fa-e77e-4d39-93dc-3c9b736a97a6","year":2022},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:6a659ad9d969c3aedbf4b8ec6290546db93def7cf86c78af4e562389207011dd","observation_id":"c2faa756-c06f-4cd1-8953-7359815b55fb","resolution":{"observed_at":"2026-05-26T00:01:24.123898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Walter de Gruyter GmbH & Co KG","venue":null,"work_id":"42732084-aa8c-4918-b2dd-1867da77a8fa","year":2023},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:f2b6a7f8772c95190ee0442cf0b3174552b3e37a6504306fe1f316b15ed870f1","observation_id":"c9dc281c-4ec5-40c1-8deb-21d363ee0a80","resolution":{"observed_at":"2026-05-26T00:01:24.110365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16785","last_updated":"2024-05-31T15:03:11Z","snapshot_observed_at":"2026-07-06T17:35:41.168780Z","submitted_at":"2024-02-26T18:00:29Z","title":"CARTE: Pretraining and Transfer for Tabular Learning","version":2},"cited_work":{"arxiv_id":"2402.16785","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.16785","snapshot_observed_at":"2026-07-01T14:25:46.567591Z","title":"2024 , month = may, number =","venue":null,"work_id":"a80521be-8779-4739-a9f7-8e167158aeed","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"cited_paper":"/paper/2402.16785","citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:a5460d60803ffe63462142686e2640dd7be1db15734f85949e9f5231247533b2","observation_id":"62f74740-8e7f-4d2f-82b6-4e4b78b3da02","resolution":{"observed_at":"2026-05-11T16:21:07.948224Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14415","last_updated":"2025-06-30T15:48:16Z","snapshot_observed_at":"2026-08-04T08:11:59.050351Z","submitted_at":"2025-05-20T14:27:51Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning","version":2},"cited_work":{"arxiv_id":"2505.14415","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14415","snapshot_observed_at":"2026-06-30T07:04:21.323657Z","title":"Table Foundation Models: on knowledge pre-training for tabular learning, May 2025","venue":null,"work_id":"152c5344-586a-4af0-b87e-cd486c09dac5","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"cited_paper":"/paper/2505.14415","citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:5b90e7b6d9a8c2fe99e203b2f007e95312367eca5ca5406a37aff9b5f9db2204","observation_id":"1577594e-4499-4b24-80e0-646c96ccf321","resolution":{"observed_at":"2026-05-11T16:21:07.886717Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rapid identification of staphylococci by raman spectroscopy.Scientific reports, 7(1):14846","venue":null,"work_id":"ec660234-0f82-4adc-b437-02500df0c56f","year":2017},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:9f6e88b6290f1242d470098115235fca6a2126b60cb157eaad7b3c1cf86f5d88","observation_id":"dd84e2f4-7077-4219-ace5-d9fa94019fd5","resolution":{"observed_at":"2026-05-26T00:01:24.084805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"How to pre-process raman spectra for reliable and stable models?Analytica chimica acta, 704(1-2): 47–56","venue":null,"work_id":"1c190f53-8af6-4f85-aa42-145b237934d4","year":2011},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:0c734c59bf4b7f6d97ae90aa7c4987a716182f8fd9ca21d48faeed4c04d6d134","observation_id":"7e519b70-6cb5-460f-8f66-d5862ba1b3f7","resolution":{"observed_at":"2026-05-26T00:01:24.089460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep convolutional neural networks for raman spectrum recognition: a unified solution.Analyst, 142(21):4067–4074","venue":null,"work_id":"f4c8afff-0e03-40ac-9fb4-67f82954be76","year":2017},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:0c240b36e8e4fdcdd657aa2b84de33611cf84f497d55e8eb792968467741c2ee","observation_id":"578d2b5e-1cb6-4cad-9aa6-6c973fdad7af","resolution":{"observed_at":"2026-05-26T00:01:24.068433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Scale-adaptive deep model for bacterial raman spectra identification.IEEE Journal of Biomedical and Health Informatics, 26(1):369–378","venue":null,"work_id":"a6e1629a-3df0-4c0b-98f1-5983730f4e40","year":2021},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:dda4961161bfb9ed60eee1585084b08feb65fecaf6b678ee385acf061d2840c0","observation_id":"6192cae8-ad75-43c7-a375-834c5d3cf8af","resolution":{"observed_at":"2026-05-26T00:01:24.071865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ramannet: a generalized neural network architecture for raman spectrum analysis.Neural Computing and Applications, 35(25):18719–18735","venue":null,"work_id":"79e2b106-68e6-47e0-8ab5-f579e0c51a7c","year":2023},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:2b0426529aba21b1e676d8500096dd57b0e6ba99c6aa248a2f37d084742db68e","observation_id":"c903ea82-483d-410b-9234-cc03d5d31bef","resolution":{"observed_at":"2026-05-26T00:01:24.061462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ramanformer: A transformer-based quantification approach for raman mixture components.ACS omega, 9(22):23241–23251","venue":null,"work_id":"63f487e7-96e3-4bbc-8b35-128057d318dc","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:a626f0bbdc95a078a19140bee3d38137a9058b64f3296c0190da5e69ec644ec1","observation_id":"e5df74e1-00d4-433e-895b-c2664893b768","resolution":{"observed_at":"2026-05-26T00:01:24.058306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep learning- based raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach.Talanta, 275:126138","venue":null,"work_id":"4434154a-a335-48db-a676-fd24ff82e97b","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:990b3b2a82ca86099071dd30cfe31f2c5aabc7f1c134044425a7b3bc633ccda6","observation_id":"7a38f11b-cb27-49d7-bf45-47a89c00def2","resolution":{"observed_at":"2026-05-26T00:01:24.064802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A self-supervised learning method for raman spectroscopy based on masked autoencoders.Expert Systems with Applications, page 128576","venue":null,"work_id":"52243b80-a491-4978-af5f-af2202145cee","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:2515dc02d32535f1c7392a22dcaec4604dd63a0b08797333888d82962ab59700","observation_id":"4527309a-f863-4201-a0fe-e90b4c92f497","resolution":{"observed_at":"2026-05-26T00:01:24.074899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Tabpfn: A transformer that solves small tabular classification problems in a second","venue":null,"work_id":"9b5ba4e0-d3fc-47d3-8df3-1e9c7d9f58e9","year":2023},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:511766f255690407f679484966ce174756c10c0098c8455b18038f3c746f66b2","observation_id":"d6ce0e42-d3e5-469c-8973-8e660ed0423e","resolution":{"observed_at":"2026-05-26T00:01:24.077949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41586-024-08328-6","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T09:06:59.042414Z","title":"Accurate predictions on small data with a tab- ular foundation model.Nature, 637(8045):319–326","venue":"Nature","work_id":"c5016413-593c-4315-8d0d-78dbbc39ac49","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:fabc7d5d8f5f0fff727da0a9df36502b5bca609f2e148839b086f808b019891a","observation_id":"f9d56a5b-1e9e-4aab-8d54-56369dbcb5d5","resolution":{"observed_at":"2026-05-09T21:38:30.542252Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-11T17:19:31.723094+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T17:19:31.723094+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"TabICL: A tabular foundation model for in-context learning on large data","venue":null,"work_id":"27bb88da-716a-4e40-a3ab-e49e624c0662","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:809ecb1d432fc6c43ddcbbbe1ebb27ef56cb7ff6d8b9425ae973dcb0269e7112","observation_id":"05c55054-0603-465b-a2e7-cc60bdfebb93","resolution":{"observed_at":"2026-05-26T00:01:24.044662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2602.11139","doi":"10.48550/arxiv.2602.11139","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"TabICLv2: A better, faster, scalable, and open tabular foundation model.arXiv preprint arXiv:2602.11139","venue":"Open MIND","work_id":"402348e3-a3ef-4455-a95a-4b7f04970bc2","year":2026},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:614af17ef1a445d5e0ac0cdc5f3fa8f289410261a0079981e52d222e8d50ed64","observation_id":"6b7f8a15-ff00-4b01-b883-45ec99f23b29","resolution":{"observed_at":"2026-05-11T16:21:07.970991Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Rocket: exceptionally fast and accurate time series classification using random convolutional kernels.Data Mining and Knowledge Discovery, 34(5):1454–1495","venue":null,"work_id":"7a1411c2-5e76-46de-b1fb-560e60b13c87","year":2020},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:15b62b8c999d1b4dd186693bf784a671842574db673d4a2d8a6c5ee2447439b6","observation_id":"bcf709e4-1d8f-439b-aba4-90bee613c38b","resolution":{"observed_at":"2026-05-26T00:01:24.048230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hive-cote 2.0: a new meta ensemble for time series classification.Machine Learning, 110(11):3211–3243","venue":null,"work_id":"51862d6a-978a-4181-91d1-0873a1d61971","year":2021},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:6c32e727cf8328f4302dd6920c6c275659f29fcdd49fc36387d564552b30d52c","observation_id":"cd22848e-9f64-456b-a1b0-cb9a7342a4c5","resolution":{"observed_at":"2026-05-26T00:01:24.054675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Massspecgym: A benchmark for the discovery and identification of molecules.Advances in Neural Information Processing Systems, 37:110010–110027","venue":null,"work_id":"fdf98ffe-4671-42bd-b88e-13339b598898","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:bd7e619156c31a92ceb73f810d954a33391852611a3979f8b778687f24e3565f","observation_id":"8e31b622-0b4f-4f67-8c23-7cc2373ba5cb","resolution":{"observed_at":"2026-05-26T00:01:24.041412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts.Nature Computational Science, 5(4):292–300","venue":null,"work_id":"6253a2d1-8fa1-4e5e-a824-a975a42f0914","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:97931a71b2a92c6a0ede47e353f5ab8135714867092e7173d57af0674ee3d4b4","observation_id":"67b9bb50-66d8-4e8f-9511-3cc5d77d94e4","resolution":{"observed_at":"2026-05-26T00:01:24.036689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"In-line monitoring of microgel synthesis: flow versus batch reactor.Organic Process Research & Development, 25(9):2039–2051","venue":null,"work_id":"2b2646f1-f6c5-4eb4-a4da-183b58e88911","year":2039},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:b84423012ee868956c279c55ffbcfbda34b6da07c3ddd6edac2b3c040fc347db","observation_id":"983725f7-9895-4718-8cb6-65bf311a4a1d","resolution":{"observed_at":"2026-05-26T00:01:24.051505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6d64c6ed-450b-4256-9243-a1f92c842ff6","year":2018},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:487a43d13def22b28acf851701131bed205bc2276403a7593d25e5d12fc04b7f","observation_id":"b7c94a32-ef16-4bcd-a79e-3e574c58e933","resolution":{"observed_at":"2026-05-26T00:01:24.081229Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Open raman spectral library for biomolecule identification.Chemometrics and Intelligent Laboratory Systems, 264:105476","venue":null,"work_id":"d0e03a8c-b6ec-4ed8-ad50-921a7bb7656c","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:0a0262d1f9333b60ebc36dfad5dc05ed1cece0753bad6d07ee93b850770e70a3","observation_id":"6ab65899-f67f-43d4-8f98-9704740c7cd3","resolution":{"observed_at":"2026-05-26T00:01:24.093027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dataset of raman and surface-enhanced raman spectroscopy spectra of illicit adulterants added to dietary supplements","venue":null,"work_id":"9e5faa29-7e80-4e2e-8b42-3e9b3f79b7f7","year":2023},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:eac191c39db60ef95ff1a2a9ee51389948448fcb354db221d268e2f2f5a1b4ad","observation_id":"eb75e834-2dd9-4ff8-8c6e-ee99b5210935","resolution":{"observed_at":"2026-05-26T00:01:24.127586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Transfer-learning-based raman spectra identification.Journal of Raman Spectroscopy, 51 (1):176–186","venue":null,"work_id":"e6b1405b-453e-465f-a39c-489eefa91787","year":2020},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:e79634c8eda686f53e683337fbc31565b90f9419a1648d0ff785e41147ecce84","observation_id":"6a1bde80-3f9b-4f8c-bfea-ef990054ccf7","resolution":{"observed_at":"2026-05-26T00:01:24.168656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.01635","last_updated":"2024-09-03T06:13:03Z","snapshot_observed_at":"2026-07-06T19:09:32.189008Z","submitted_at":"2024-09-03T06:13:03Z","title":"PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications","version":1},"cited_work":{"arxiv_id":"2409.01635","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2409.01635","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pmlbmini: A tabular classification benchmark suite for data-scarce applications","venue":null,"work_id":"57162a40-15a8-40be-be1d-4bb055ae018f","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"cited_paper":"/paper/2409.01635","citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:2ed213d8b6636c03e365eadc0fcd09a8fe1678ae732aca9d576eb6d28998dcc5","observation_id":"25e50d53-f7ed-4354-a844-7cfa14785547","resolution":{"observed_at":"2026-05-11T16:21:07.910134Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Revisiting deep learning models for tabular data.Advances in neural information processing systems, 34: 18932–18943","venue":null,"work_id":"9d0504ec-173e-423b-939d-f6d895151f5f","year":2021},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:06426363da6a79d3c5c5181b96eeb688b9a3dbb6b1c539a9b0570bbb13f8264d","observation_id":"9a619f90-fb57-446e-9b57-33b5f930565e","resolution":{"observed_at":"2026-05-26T00:01:24.022375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T07:34:43.964411Z","title":"Rezero is all you need: Fast convergence at large depth","venue":null,"work_id":"e9b45e36-1d11-4257-8120-0213ff4c4645","year":2021},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:aa3aeb4eb1a3e1dc6286a2b8f0c832a0dd92628c7cb86bf10bf599f689e6a163","observation_id":"18b17b5b-a12f-4efd-95bb-895b932b30ec","resolution":{"observed_at":"2026-05-26T00:01:24.025598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2402.03970","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T22:06:16.746526Z","title":"Is deep learning finally better than decision trees on tabular data?","venue":null,"work_id":"00a0abfe-d46b-47d4-87a7-cae4d66a5e68","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:40106cab08602db8d7fc788badeb6138517d978a8782765d30888f539116b7eb","observation_id":"9c222e98-c6d9-427c-974f-52e022217ac4","resolution":{"observed_at":"2026-05-11T16:21:07.900090Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Coatnet: Marrying convolution and attention for all data sizes.Advances in neural information processing systems, 34:3965–3977","venue":null,"work_id":"e6ea4213-2d89-449e-b866-85df09501e85","year":2021},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:4a686328ef35e6453de45a9f2448d79058757963585ef96cc8531f13407df090","observation_id":"92ecc927-c506-4830-98f0-75312683b5c1","resolution":{"observed_at":"2026-05-26T00:01:24.014401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.21204","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T15:49:57.792334Z","title":"Mitra: Mixed synthetic priors for enhancing tabular foundation models.arXiv preprint arXiv:2510.21204, 2025b","venue":null,"work_id":"13ac8908-32c9-4d6b-8a80-8cdbb48630c3","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:f9321f9207a1c9a8682d293b32e257ab306d71c58b830abc10d286f68555fac9","observation_id":"442401f1-816e-406e-af5b-55a97ac990f7","resolution":{"observed_at":"2026-05-11T16:21:07.989231Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2410.18164","doi":"10.48550/arxiv.2410.18164","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"TabDPT: Scaling tabular foundation models on real data.arXiv preprint arXiv:2410.18164","venue":"arXiv (Cornell University)","work_id":"6dea29bf-5c1e-4430-8f09-25c430201a44","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:469d027f94d46653bf96469513fe99cf1cf26f1b020821f33aa8c6ffb9119268","observation_id":"7a75b104-47e9-4ace-853d-b511c15bf1dd","resolution":{"observed_at":"2026-05-11T16:21:07.917910Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.24210","last_updated":"2025-02-18T18:58:14Z","snapshot_observed_at":"2026-07-06T19:43:05.453551Z","submitted_at":"2024-10-31T17:58:41Z","title":"TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling","version":3},"cited_work":{"arxiv_id":"2410.24210","doi":"10.48550/arxiv.2410.24210","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.24210","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"arXiv preprint arXiv:2410.24210 , year=","venue":"arXiv (Cornell University)","work_id":"888b0962-d524-444b-942c-791800424fea","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"cited_paper":"/paper/2410.24210","citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:b636cb29398f1441aae33bf7103bfe1fe73efcb5b4feb8f63d7a551e1d2b4476","observation_id":"b14f14fd-b538-4784-bf7f-3c40c8355aba","resolution":{"observed_at":"2026-05-11T16:21:07.926840Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Classification of deep-sea cold seep bacteria by transformer combined with raman spectroscopy.Scientific Reports, 13(1):3240","venue":null,"work_id":"394fafa5-6fce-4bdc-9dcd-c32849308991","year":2023},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:e1adbb8e7a67c1436e415860804986d7af6d0490dc8c3aeef834e4c77f818044","observation_id":"89e4f3af-684b-4cb3-bd56-af83ae6830bf","resolution":{"observed_at":"2026-05-26T00:01:24.018136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.06505","last_updated":"2020-03-13T23:10:39Z","snapshot_observed_at":"2026-07-06T09:04:37.917150Z","submitted_at":"2020-03-13T23:10:39Z","title":"AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data","version":1},"cited_work":{"arxiv_id":"2003.06505","doi":"10.48550/arxiv.2003.06505","metadata_source":"pith","pith_arxiv_id":"2003.06505","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data","venue":"stat.ML","work_id":"32ca4e6c-bd72-4586-8594-40eb6bcb6582","year":2020},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"cited_paper":"/paper/2003.06505","citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:b85f491816c4cdff7ec31082eda5a045c190e9ebf1a225ee31ee7c8e90f33895","observation_id":"2447676b-643e-4b07-9ae0-402fdd3f5480","resolution":{"observed_at":"2026-05-15T10:34:31.897638Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-05-23T01:54:03.658463+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T01:54:03.658463+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"TabRepo: A large scale repository of tabular model evalua- tions and its AutoML applications","venue":null,"work_id":"c96cfa46-c832-4292-b96f-87b3f18117d8","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:6dd792150ec935c3b571654a43973459844f1cccb42abb86ec80793d071a4322","observation_id":"1c212300-48d6-4640-85d4-818028255034","resolution":{"observed_at":"2026-05-26T00:01:24.029514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Statistical comparisons of classifiers over multiple data sets.Journal of Machine learning research, 7(Jan):1–30","venue":null,"work_id":"f257b987-9551-47e1-afa5-3b7b8e87380a","year":2006},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:4988b7e6789259f68553bd73ee97c0a750a8fd7c1536dc629b8dfbaf809bad39","observation_id":"1323a336-25a0-4135-8b40-9f40ad169f1a","resolution":{"observed_at":"2026-05-26T00:01:23.999248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"8934a0f0-0ef2-44f3-b8e5-92cc6d334130","year":2023},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:9d0651a4a39e76ce90363afb604a0e9838c8ca5848e5f9a5cc46c6eab787a8c5","observation_id":"94225840-a9ef-40d0-a1a9-06d9959d40a8","resolution":{"observed_at":"2026-05-26T00:01:24.010450Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1087/20150211","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learned Publishing , volume =","venue":"Learned Publishing","work_id":"18521a42-221c-45d9-9bcb-ffd7ec02ddf7","year":2015},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:da0ceb04f25d8e9947a184917f539fdbc5f81c22f4af7148ddb482012f170ede","observation_id":"0f44b8d8-2a7d-4498-9653-af53219e089b","resolution":{"observed_at":"2026-05-09T21:38:30.528830Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-06-01T21:26:54.109724+00:00","source":"crossref_status_cache"},{"observed_at":"2026-06-01T21:26:54.109724+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T16:33:56.596783Z","title":"The proposed uscf rating system, its development, theory, and applications.Chess life, 22(8):242–247","venue":null,"work_id":"e1112580-1799-4571-8157-887321593ccf","year":1967},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:d5f0358342efe68ab86436387929b0d09d03c9cf4ebcc6b947219dfdd658f4f4","observation_id":"5ab421d3-ca6f-4493-b4bc-80e4de61a332","resolution":{"observed_at":"2026-05-26T00:01:24.003179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.21105/joss.02173","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Journal of Open Source Software5(48), 2173 (2020)","venue":"The Journal of Open Source Software","work_id":"bdb01561-ffeb-4715-8e96-1961ec1afdc6","year":2020},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:e1eb19ab40146e4c1be59bd335d966080d0ebd31d1f738dfef3b20cf9bbcb390","observation_id":"40e83690-445d-4dde-a980-8dd398ac6a36","resolution":{"observed_at":"2026-05-09T21:38:30.539437Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T00:35:48.974779Z","title":"Xception: Deep learning with depthwise separable convolutions","venue":null,"work_id":"63e2cc3b-1c48-473f-bb23-918a0814b60d","year":2017},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:59b98c26fefe0f42c5c9b99db449c076a970b7e82a623c76504ff42e4a1cbaf6","observation_id":"c18241f4-3016-4622-bcbc-72b5cf94d063","resolution":{"observed_at":"2026-05-26T00:01:23.975856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A new ecoc algorithm for multiclass microarray data classification","venue":null,"work_id":"b3783a70-1999-464b-b6dc-8ef6d5a6a0bb","year":2018},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:d9b5be0f33a028c2e74d4ea7524881b9dbcfb3fb82a8d2a4523531d75359cadd","observation_id":"30909bce-95ac-4f64-9d95-ad9344cd2ad7","resolution":{"observed_at":"2026-05-26T00:01:23.979254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A setup for automatic raman measurements in high-throughput experimentation.Biotechnology and Bioengineering, 122 (10):2751–2769","venue":null,"work_id":"7a41dd01-6f40-4979-93c7-618fea5c30b9","year":2025},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:74a23dde76c025c708ad88dce23551671be8b6abffe073fa4c23071c7910f452","observation_id":"26cf6dfe-80d7-40b4-9690-48ed39a9f1e9","resolution":{"observed_at":"2026-05-26T00:01:23.960784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Application of green analytical chemistry to a green chemistry process: Magnetic resonance and raman spectroscopic process monitoring of continuous ethanolic fermentation","venue":null,"work_id":"ed7c4aee-1fce-4eba-aeb4-583489b5316f","year":2019},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:79057e235437079e52b6756a14a38fd9dabbe3c6c33f6330d3448d6ddd491c9a","observation_id":"b34883ed-e3b1-4406-ae4a-aeb5300eb1b2","resolution":{"observed_at":"2026-05-26T00:01:23.964339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Data augmentation scheme for raman spectra with highly correlated annotations","venue":null,"work_id":"b0185b23-730f-4b00-aadb-8ee8f163316c","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:95ee92d34639e1be06cf8a989e5989cb8849dc185ec16be772c8cc55bb5bb7db","observation_id":"9ba9a3b6-00b5-456d-825c-760e797ba339","resolution":{"observed_at":"2026-05-26T00:01:23.982583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3feaac63-da01-4bbd-bcab-a1821bcd3d70","year":2019},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:d34cf9fa684460afc9d3b87bf837eb1d5f97c4cea01c17f356d9a54516163b49","observation_id":"6d33ea44-85ef-43e7-98c9-1c1dbbb69ee2","resolution":{"observed_at":"2026-05-26T00:01:23.954693Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"973aa385-ba87-4d20-ae13-8383df5c1c19","year":2019},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:4cf08e8dbd592fa573d81dd2324f96a535d9fda4635431c88e9e99862ea985af","observation_id":"8a21bd47-f673-4366-8c48-17f8512c0fee","resolution":{"observed_at":"2026-05-26T00:01:23.967609Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Surface enhanced raman spectroscopy for quantitative analysis: results of a large- scale european multi-instrument interlaboratory study.Analytical chemistry, 92(5):4053–4064","venue":null,"work_id":"44b751fc-bdb0-4d1d-8847-a5aa9c6601e2","year":2020},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:1b816ac60a130b32fefa4fb1be29275ee1de08136bdb2beacbd39017c5733acd","observation_id":"bdf1768e-f10d-4525-9e9e-66e7b21f3cbf","resolution":{"observed_at":"2026-05-26T00:01:24.006787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Identification of synthetic organic pigments: the role of a comprehensive digital raman spectral library.Journal of Raman spectroscopy, 43(11): 1536–1544","venue":null,"work_id":"f681682e-257f-4872-b1a7-b3fed49f55e0","year":2012},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:fb529dfbaa830d446fccad3c62312806bc61140215d508866bb7fe0536636ea3","observation_id":"bddd76cf-4cbe-443f-a453-0186803e9b37","resolution":{"observed_at":"2026-05-26T00:01:24.032988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Raman spectra and surface changes of microplastics weathered under natural environments.Science of the Total Environment, 739:139990","venue":null,"work_id":"ab5fefd1-1ecf-4f3d-ab35-ae610ad74ddb","year":2020},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:6717c9f8a4bb1e188f3bee6ca15776c58f3079e710dd922fc766bd8f81579f3b","observation_id":"d70d7eb3-020e-4691-b1c6-f9e26b61a371","resolution":{"observed_at":"2026-05-26T00:01:23.948877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4eaa82ff-3b1e-45a1-ba87-6e5390dd431a","year":2020},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:9bedd957fb7f0bb33c64eebaaa3ae8d72d86388294cf4aeca5f4397513a338c4","observation_id":"aefdc29e-e5c6-449a-a193-d3e411738f4a","resolution":{"observed_at":"2026-05-26T00:01:23.951978Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Differentiation of advanced generation mutant wheat lines: Conventional techniques versus raman spectroscopy.Frontiers in Plant Science, 14:1116876","venue":null,"work_id":"4e38c627-c5f8-4cf4-91f4-e1d8aec121c6","year":2023},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:19a337ee95183f7a77fca58ea37e8302dfe0f0b3430d0cfd15c1be2c2b78b8e0","observation_id":"864a6178-ddf4-40fd-9b41-22d4d8fe5dd8","resolution":{"observed_at":"2026-05-26T00:01:23.939507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Use of raman spectroscopy to screen diabetes mellitus with machine learning tools.Biomedical Optics Express, 9(10):4998–5010","venue":null,"work_id":"e347758f-e4a6-4bb7-ad8d-0af0feaff2d0","year":2018},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:28e6d178b33815c4c9f16e8699171f756ab26657ce3da82f4490abb889404796","observation_id":"df14c789-4313-4876-8b4d-e6c5bd11d39e","resolution":{"observed_at":"2026-05-26T00:01:23.932125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fused raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics.Scientific Reports, 12(1): 18464","venue":null,"work_id":"ffe36785-b4ad-4aae-9356-33fa14d413c0","year":2022},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:7eaab0a3d457ac7da455c4f68fc8a04c4555017a016e7b9d218091d01eb9c0df","observation_id":"e1f693b4-83d6-4502-a129-108fcc30d037","resolution":{"observed_at":"2026-05-26T00:01:23.945462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.07470","last_updated":"2020-11-15T07:57:03Z","snapshot_observed_at":"2026-07-06T10:14:38.654601Z","submitted_at":"2020-11-15T07:57:03Z","title":"An efficient label-free analyte detection algorithm for time-resolved spectroscopy","version":1},"cited_work":{"arxiv_id":"2011.07470","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2011.07470","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An efficient label-free analyte detection algorithm for time-resolved spectroscopy","venue":null,"work_id":"142498a7-f8e0-49a8-9a08-3ce342dc70c1","year":2011},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"cited_paper":"/paper/2011.07470","citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:b597c76833563862a5a56cd0a31a139e1da235b017b1556374699037f21a31ed","observation_id":"4984951c-ded0-438e-8cac-7a689a500d6b","resolution":{"observed_at":"2026-05-11T16:21:08.011758Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Surface-enhanced raman spectroscopy enables highly accurate identification of different brands, types and colors of hair dyes.Talanta, 251:123762","venue":null,"work_id":"9ed30069-cb11-48d3-98d9-e0aabe375fef","year":2023},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:da640be1a4d08d8d4a1b386eefba2c135785812c63289234f26507356b334055","observation_id":"a1890699-237d-439f-b75e-aa711788777d","resolution":{"observed_at":"2026-05-26T00:01:23.914980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nonlinear manifold learning determines microgel size from raman spectroscopy.AIChE Journal, 70(10):e18494","venue":null,"work_id":"c3f0efa7-7018-4034-96df-a14e15616253","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:d6fe4125f178cc5a7e7b4599b13924c0473f226116707bebf9abbb5e0b8b67d4","observation_id":"9da5e227-228b-4943-8bb5-913d88c7213c","resolution":{"observed_at":"2026-05-26T00:01:23.919431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Data-driven product-process optimization of n-isopropylacrylamide microgel flow- synthesis.Chemical Engineering Journal, 479:147567","venue":null,"work_id":"0c6f7cff-c579-4cba-9d78-d66f9ff1b07a","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:63de5c2347f9dff8acabd051e3a6ed11f5d0d4250f8fcb100dc0c6e4637b6dbe","observation_id":"742ee74b-f821-4620-9184-7196dc9284c4","resolution":{"observed_at":"2026-05-26T00:01:23.925802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Limitations","venue":null,"work_id":"7fd685fe-2dc4-4963-82c8-6c45a303944f","year":2024},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:4e98cdf05efa1638dc7db7bba422673b54eceac9f205354c3826b3c98eabaa26","observation_id":"3cce7328-1e65-40a0-afe9-02d11d6a3509","resolution":{"observed_at":"2026-05-26T00:01:23.936277Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"bioprocess_substrates","venue":null,"work_id":"fa5e8cc8-09bf-4ec1-9509-9e0d5b96743d","year":null},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:8f589c50db089ce2bb49b83f095e6a286b27c4e885acb0c5e1ee3fb45020b427","observation_id":"d9c0a16c-182a-49b9-9d18-ff737dbeaee3","resolution":{"observed_at":"2026-05-26T00:01:23.942271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Outlier results trigger manual inspection of the submitted code or weights; models with confirmed irregularities are flagged on a separate leaderboard","venue":null,"work_id":"26ab1fd9-da01-4434-bcdc-b8120af7dfab","year":null},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:7a59ba39671ba4beb4eb291f6c81b78f3f064a4764a2fab6be79e55574c40b3a","observation_id":"29eb9f14-0580-4d2f-adbf-9edffc1a6f86","resolution":{"observed_at":"2026-05-26T00:01:23.907819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"gold nanourchins","venue":null,"work_id":"f565305e-6227-45ce-ac77-5f02ff30f8c3","year":1990},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:b1eccfdaa9bdc7de95f2f4832baa871df3dcb047a9b246d47fe8e7ad6710121b","observation_id":"b5e50c2a-f3e4-4cad-81f8-ba232312d160","resolution":{"observed_at":"2026-05-26T00:01:23.904593Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"aureusstrains for antibiotic susceptibility classification","venue":null,"work_id":"ef370cda-ae0a-48f4-af4d-ffff421b53f4","year":2000},"citing_paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-05-09T17:21:37.982077Z"},"links":{"citing_paper":"/paper/2605.02003"},"observation_digest":"sha256:c3ecb1882fbf1dfa210328e59f052a261bbf0a1d5d8c8fa963f319f56bc8568b","observation_id":"a42650a1-97c0-4d98-a862-d10fceb74fb5","resolution":{"observed_at":"2026-05-26T00:01:23.911556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.02003","last_updated":"2026-05-06T11:03:18Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-02T08:59:44.247004Z","submitted_at":"2026-05-03T18:12:42Z","title":"RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy"},"reference_resolution":{"displayed":96,"state_counts":{"malformed_identifier":2,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":6,"verified_exact":20,"verified_fuzzy":65},"total_outbound_references":96},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 96 of 96 outbound references and 1 inbound Pith citation observation for arXiv:2605.02003."}