{"as_of":"2026-08-22T16:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9b909013240c372cf5078379678c31735556474f281192a2e54eee9602692872","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T17:42:58.921815Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2508.02625/citation-record","integrity":"/paper/2508.02625/integrity","json":"/paper/2508.02625/citation-record.json","paper":"/paper/2508.02625"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2207.08815","last_updated":"2022-07-18T08:36:08Z","snapshot_observed_at":"2026-08-16T16:45:15.654938Z","submitted_at":"2022-07-18T08:36:08Z","title":"Why do tree-based models still outperform deep learning on tabular data?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.08815","snapshot_observed_at":"2026-08-15T17:42:58.781389Z","title":"Why do tree- based models still outperform deep learning on tabular data?","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.781389Z"},"links":{"cited_paper":"/paper/2207.08815","citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:77216635d15de2ff4e2846aa4b72c0f1468e7f9da131a1fe1f6dffde9ee3eb6e","observation_id":"42de993c-8ae0-4a14-ba91-f2ad22e9548b","resolution":{"observed_at":"2026-08-15T17:42:58.781389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:59.669071Z","title":"Why do tree-based models still outperform deep learning on tabular data?","venue":null,"work_id":"ed0161be-d4da-47d6-9176-76ca85726be0","year":2022},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.788409Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:2edff53aa56ff8cd8e6c45b8768426a6c7d0b789c7825624a78ec9374e5f3fba","observation_id":"3ce59960-fc33-445c-80d2-db9db4e93ad7","resolution":{"observed_at":"2026-08-15T17:42:59.674301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.01889","last_updated":"2022-06-29T16:14:08Z","snapshot_observed_at":"2026-08-16T17:51:50.173970Z","submitted_at":"2021-10-05T09:22:39Z","title":"Deep Neural Networks and Tabular Data: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.01889","snapshot_observed_at":"2026-08-15T17:42:58.794241Z","title":"Deep neural networks and tabular data: A survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.794241Z"},"links":{"cited_paper":"/paper/2110.01889","citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:16745eb18d00838e58847b8fe3f3875b4e2a07ece411c3dd3d6907c7382b2388","observation_id":"4473f2dd-8f0a-48b4-ba6c-2e839aeb703a","resolution":{"observed_at":"2026-08-15T17:42:58.794241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:59.652263Z","title":"Kuhn and K","venue":null,"work_id":"fdff6cf2-c215-47e0-8b48-0d41410baa2d","year":2019},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.800956Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:44a3678864cf10bd06308036b776bc750223577898659edb6d243429485c2010","observation_id":"dac2164e-b21b-41b2-b3b7-f23255f6c1c5","resolution":{"observed_at":"2026-08-15T17:42:59.657369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T17:42:59.631527Z","title":"Data preprocessing in data mining,","venue":null,"work_id":"858f68ef-3839-45a1-b10b-34b25b40517a","year":2015},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.807206Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:17830eff866d64411a64e869071e504480faa2a9d9c52978f35bd04bb9808352","observation_id":"de3e93f5-54f2-442b-95c3-493d03bd9ae1","resolution":{"observed_at":"2026-08-15T17:42:59.638667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T17:42:59.610615Z","title":"Handling class imbalance in medical datasets,","venue":null,"work_id":"38664b6c-34fa-4d4a-9014-389b378f785a","year":2013},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.812956Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:7e31a72097e4838ceb0b994a356993b46db809228b49ea5dc03fef61adc6d387","observation_id":"063c67c2-a7c5-473b-b9da-368410bfd26a","resolution":{"observed_at":"2026-08-15T17:42:59.616376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T17:42:59.590719Z","title":"A survey on feature selection methods,","venue":null,"work_id":"89adfa47-45f6-4ead-80e8-739af9693977","year":2014},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.818561Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:8f5430f18cb30d6d71866c975628c7b9dd238a8aa60947574b8370e753ae82ad","observation_id":"32872992-47c7-4fd0-8679-b75009c53fa1","resolution":{"observed_at":"2026-08-15T17:42:59.597019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:58.823966Z","title":"Feurer, A","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.823966Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:f35c5fe278d3f2b91986c72b10908c2f8fc5fba9d5f06ed5d6dfdc61c7cc2b54","observation_id":"31725736-19cf-4770-b1ff-c19cee5d12a7","resolution":{"observed_at":"2026-08-15T17:42:58.823966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1201.0490","last_updated":"2018-06-05T13:41:07Z","snapshot_observed_at":"2026-08-15T04:24:00.936483Z","submitted_at":"2012-01-02T16:42:40Z","title":"Scikit-learn: Machine Learning in Python","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1201.0490","snapshot_observed_at":"2026-08-15T17:42:58.829025Z","title":"Scikit-learn: Machine learning in python,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.829025Z"},"links":{"cited_paper":"/paper/1201.0490","citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:33a3c3767c373de15e82dead661a0a01c736e3839972dbdbec65dc968c60b77a","observation_id":"a5def450-29f7-42d4-a4cc-722b3a334cfa","resolution":{"observed_at":"2026-08-15T17:42:58.829025Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:59.568576Z","title":"H2o automl: Scalable automatic machine learning,","venue":null,"work_id":"5a7beccd-707b-480a-934f-3bb61e55f3b9","year":2020},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.834121Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:daba6f6d74388585a3db2e122136cd6ad4bf60956e2cf9ea7f0a464bef686ace","observation_id":"cb11c2df-70be-4e9a-be18-05be7eb030d4","resolution":{"observed_at":"2026-08-15T17:42:59.574581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:58.838961Z","title":"GAMA: A general automated machine learning assistant,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.838961Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:7bd5b5caec2a3e4f4a4519148cc0fe3e03771407970f4863cad477f65b674215","observation_id":"f8a99a13-ca0c-4ef4-a11a-03b8cde64028","resolution":{"observed_at":"2026-08-15T17:42:58.838961Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:58.843976Z","title":"Evaluation of a tree-based pipeline optimization tool for automating data science,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.843976Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:462821275015615bf21163eeae7cb4adf42b3b9f56390e95b9e49785b5f7604d","observation_id":"ac29df29-d941-4973-bf90-4cfbc1846c85","resolution":{"observed_at":"2026-08-15T17:42:58.843976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:58.849281Z","title":"TPOT2: A new graph-based implementation of the tree-based pipeline optimization tool for automated machine learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.849281Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:2e6a00dd027ed8a3364c7623baa8bec986c51695028107a6ebecf37b7f9da556","observation_id":"1cdf1606-b409-4ba3-9af1-9a00c84af0ac","resolution":{"observed_at":"2026-08-15T17:42:58.849281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1808.06492","last_updated":"2018-08-17T02:15:39Z","snapshot_observed_at":"2026-08-14T18:40:08.970382Z","submitted_at":"2018-08-17T02:15:39Z","title":"Benchmarking Automatic Machine Learning Frameworks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.06492","snapshot_observed_at":"2026-08-15T17:42:58.854808Z","title":"Benchmarking automatic machine learning frameworks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.854808Z"},"links":{"cited_paper":"/paper/1808.06492","citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:122cced0912ad1591fd450a501c34b037d20beb96f7ede79c18fdc7152d6318c","observation_id":"a3c23d2b-5475-49a4-b26c-0fad48fd1fcd","resolution":{"observed_at":"2026-08-15T17:42:58.854808Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.00376","last_updated":"2022-11-01T10:43:48Z","snapshot_observed_at":"2026-08-16T16:19:31.371947Z","submitted_at":"2022-11-01T10:43:48Z","title":"Automated Imbalanced Learning","version":1},"cited_work":{"arxiv_id":"2211.00376","doi":"10.48550/arxiv.2211.00376","metadata_source":"pith","pith_arxiv_id":"2211.00376","snapshot_observed_at":"2026-08-15T18:16:14.067578Z","title":"Automated Imbalanced Learning","venue":"cs.LG","work_id":"9d544c19-9496-48d7-83da-496eff5721c2","year":2022},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.860177Z"},"links":{"cited_paper":"/paper/2211.00376","citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:f43ad92422695a8fb552bd694e5c4d6a588ca454fe8980bdf8798bd1ef7a2579","observation_id":"fe8e18f8-6979-4968-a0e9-3f2b04874b7a","resolution":{"observed_at":"2026-08-15T17:42:59.006185Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s13040-022-00300-2","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:58.976132Z","title":"Benchmarking automl frameworks for disease prediction using medical claims,","venue":null,"work_id":"c06c5227-a5a2-4c5f-826b-1b35b0312e44","year":2022},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.865981Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:134c7d514d8cb50b5a2da2f9e1fce90caca7cdef9daeca2f62b82f18275bcc5f","observation_id":"646b65a6-a2a6-4f95-8dd4-c49df0c97a2c","resolution":{"observed_at":"2026-08-15T17:42:58.981769Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:58.871057Z","title":"Automated machine learning: Review of the state-of-the-art and opportunities for healthcare,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.871057Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:f428d50b1818c97d4bd4f2b1e2cd376fe9d7efced28de2c322ae6edc60fdfd6d","observation_id":"3dc352fd-0f4d-4ede-a461-27c31b36a75d","resolution":{"observed_at":"2026-08-15T17:42:58.871057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:59.550043Z","title":"A method- ology for performing global uncertainty and sensitivity analysis in systems biology,","venue":null,"work_id":"f2ccd753-6d17-44ff-9633-f6774405ea39","year":2008},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.876211Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:89d2a112e81b5f34c86abd6938ff46c36ed99c7b0993d00c4c3c2c45128afd7f","observation_id":"a9da2726-6707-440d-a018-8583f5a96afe","resolution":{"observed_at":"2026-08-15T17:42:59.555961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T17:42:59.527128Z","title":"Comparison of 3 methods for selecting values of input variables in the analysis of output from a computer code,","venue":null,"work_id":"f0119d93-e920-490c-9ca3-35cfcea6c368","year":1979},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.881148Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:3024d6fd55d342196feeb9aeeee03434bb94adfc7ecf215f9b25bf1c1cba9a6f","observation_id":"85bdfef9-60c0-486a-9d64-8adffa5d219a","resolution":{"observed_at":"2026-08-15T17:42:59.533445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T17:42:59.505748Z","title":"Non-parametric statistics in sensitivity analysis for model output: A comparison of selected techniques,","venue":null,"work_id":"b43bd380-f033-40d5-b7ab-7a6ffdb1b6b4","year":1990},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.885910Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:68d137bae6e402394b673e352212042cf2f797e21f88110cc2552df6ccaa0cff","observation_id":"6ce7ca00-6b30-40ce-af1b-497f09d1f8e2","resolution":{"observed_at":"2026-08-15T17:42:59.512920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s11538-024-01393-y","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-20T11:03:42.707129Z","title":"Examining the influence of nondimensionalization on partial rank correlation coefficient results when modeling the epithelial mesenchymal transition,","venue":"Bulletin of Mathematical Biology","work_id":"a71e76f6-7a88-4289-a5ae-cb13cc5153bf","year":null},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.891161Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:21109e8a6ebebeb104f9e37c430f229416fb6a25814bffe272de13ac1630cce7","observation_id":"bbb023f8-d853-442f-ae57-93c06b1cd9e3","resolution":{"observed_at":"2026-08-15T17:42:58.963763Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"gov/3153856","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:42:59.152246Z","title":"Building risk prediction models for type 2 diabetes using machine learning techniques,","venue":null,"work_id":"302b1d22-1d0c-4bd4-9691-eda41635667f","year":2019},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.896276Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:753c67fc53acad88be18ae67dcba98eaeb6785b08706968415c882ed5d892927","observation_id":"293a75af-58f6-4535-8053-eeff6562fda4","resolution":{"observed_at":"2026-08-15T17:42:59.161240Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T17:42:59.485745Z","title":"The immunology of multiple sclerosis,","venue":null,"work_id":"369351ca-6de1-47ad-97e7-b3afe0f48d35","year":2022},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.901583Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:a02e1e5e22cfa08b28c9db29fdc676d3fb11d0b277b5a9ff7cc1f58cdec2f546","observation_id":"8c24705f-a9af-4234-89b5-2a12e158bd56","resolution":{"observed_at":"2026-08-15T17:42:59.491937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T17:42:59.466209Z","title":"Multiple scle- rosis genomic map implicates peripheral immune cells and microglia in susceptibility,","venue":null,"work_id":"48a57e9f-7d3c-4194-8167-de2d2d3eb881","year":2019},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.906528Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:9d57dac5006fdb53670198bbef983d491f77d1f67bb0566f998b2540ede1cc3f","observation_id":"20f19b3c-e403-4dda-9e85-6d5742f1b9c2","resolution":{"observed_at":"2026-08-15T17:42:59.472264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T17:42:59.443104Z","title":"Lifestyle and environmental factors in multiple sclerosis,","venue":null,"work_id":"825d6b23-9eef-4b46-ba19-eb7c6f0d17be","year":2019},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.911558Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:8f3a529ab76bd956a0282da7d95c1df632fd246b5a810cdb6f323d6cb55319d6","observation_id":"6ffdea21-0e0a-4948-9be3-121ba8f230f1","resolution":{"observed_at":"2026-08-15T17:42:59.449414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T17:42:59.423858Z","title":"Rising prevalence of multiple sclerosis worldwide: Insights from the atlas of ms, third edition,","venue":null,"work_id":"0d8db6ec-1f9d-4e0a-976b-5c9c4bb4d111","year":2020},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.916632Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:a66e674850d102fbef99358f4ce2823a1e536807d04fbb98fe87ba538c61ecf0","observation_id":"7871df91-36af-49a6-8971-854a0daec2a1","resolution":{"observed_at":"2026-08-15T17:42:59.429835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-15T17:42:59.405922Z","title":"Economic costs of diabetes in the U.S. in 2007,","venue":null,"work_id":"a5ac9ed7-20b1-48ce-9c1f-11c5d42a72af","year":2007},"citing_paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T17:42:58.921815Z"},"links":{"citing_paper":"/paper/2508.02625"},"observation_digest":"sha256:7a3e1040a4dba80d404b8629996bb4fb66444420e1eef4689697be3a6ac1262f","observation_id":"330f2eb9-3d76-4624-b606-5dd1f54e9bce","resolution":{"observed_at":"2026-08-15T17:42:59.411720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.02625","last_updated":"2025-08-04T17:13:45Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T17:35:11.205884Z","submitted_at":"2025-08-04T17:13:45Z","title":"AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":4,"verified_fuzzy":14},"total_outbound_references":27},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2508.02625."}