{"as_of":"2026-08-07T21:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:464e5282c46281070cb8371e15ac5b1a43860305ea6e4342aea11e14750794f3","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-10T16:29:23.355323Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2607.07863/citation-record","integrity":"/paper/2607.07863/integrity","json":"/paper/2607.07863/citation-record.json","paper":"/paper/2607.07863"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2500.333070","doi":"10.1145/3292500.3330704","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Akiba, S","venue":null,"work_id":"e77359e1-10ba-4052-b8cd-6b783f141bb6","year":2019},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:6fdac3890c53e9847bdaaafb508e8ac7247cfb2d26909412c1a3930400a820bb","observation_id":"fae30d69-a64e-4a8f-b6e5-c3af68c4dfb0","resolution":{"observed_at":"2026-07-10T16:37:22.885377Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00170-010-2662-y","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"N., Etxeberria, I., Suárez, A.: Effect of process parameter on the kerf geometry in abrasive water jet milling","venue":"The International Journal of Advanced Manufacturing Technology","work_id":"9762200e-58c9-40d9-aaa9-e658f14e1fc6","year":2010},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:d07f05a7d3f849a0fea35329adc5e502a1528efb0934928555f63ba490113972","observation_id":"23275284-13a6-46e9-9ffa-3f421e4f08ac","resolution":{"observed_at":"2026-07-10T16:37:22.874383Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1561/2200000101","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T19:57:33.810801Z","title":"Angelopoulos and Stephen Bates","venue":"Foundations and Trends® in Machine Learning","work_id":"885296fd-199f-4f2b-86b9-15a3bc467158","year":2023},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:80db9da22b736344fa9fb4dfa71abda67d01b778b5470492b7de1a8a4d8634ea","observation_id":"95257b64-3723-4d98-8823-988e5aa05824","resolution":{"observed_at":"2026-07-10T16:37:22.889940Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-12T03:19:22.407825+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T03:19:22.407825+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-10T16:37:23.599747Z","title":"C.: On over-fitting in model selection and subsequent selection bias in performance evaluation","venue":null,"work_id":"9b138f88-c62c-4202-bea0-bdfbf88832b2","year":2079},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:090cac515e194b906df45d772d3dc2f0c2ec7664aabb1167579da26cdd63280a","observation_id":"276fba1f-bc9f-4692-8b08-ebc05c58df86","resolution":{"observed_at":"2026-07-10T16:37:23.601244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.jmapro.2024.01.010","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Journal of Manufacturing Processes110, 291–302 (2024)","venue":"Journal of Manufacturing Processes","work_id":"76fe618a-06e2-44f7-b000-681cf4969ee2","year":2024},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:0c24a8f04d486cb24ef7ce6e2d54c76b298ba168e2864250d6b06c6624cac381","observation_id":"20f97cdf-3078-4f3b-98ba-1e4876127d73","resolution":{"observed_at":"2026-07-10T16:37:22.887253Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1017/dce.2024.2","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Data-Centric Engineering5, e8 (2024)","venue":"Data-Centric Engineering","work_id":"192d6213-c572-4cc6-bb53-4f9091fc9730","year":2024},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:33402486743397e7fac1d655debbfbb846e27e0f6ea1e9297fb8e66c6dcee182","observation_id":"7cfde4cf-50be-4533-ac6e-997583f439ef","resolution":{"observed_at":"2026-07-10T16:37:22.854853Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00170-023-11275-7","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"The International Journal of Advanced Manufacturing Technology126, 3133–3148 (2023)","venue":"The International Journal of Advanced Manufacturing Technology","work_id":"3b0b02c8-ffcb-4909-82a4-c5a61067dab0","year":2023},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:339937895fdea8bb70e23e0f618fc5492ad2913db0e022d4dd8d7e99faca831a","observation_id":"9bd498bc-2e42-45df-8e03-0cd00589aace","resolution":{"observed_at":"2026-07-10T16:37:22.889043Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.strusafe.2008.06.020","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi: https://doi.org/10.1016/j.strusafe.2008.06.020","venue":"Structural Safety","work_id":"037cc08c-773b-42bd-b2cf-66c2caa0abba","year":2009},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:415392c377c9e58c73da74e3f6bd5397e88fa098a162e05182981e01d6090187","observation_id":"876af788-9ac4-4c44-9e93-0bb58924b8f7","resolution":{"observed_at":"2026-07-10T16:37:22.870632Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.jmsy.2024.04.001","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Journal of Manufacturing Systems73, 898–912 (2024)","venue":"Journal of Manufacturing Systems","work_id":"0ff2caa7-888d-4674-ab2c-dd1842c4ab3f","year":2024},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:bd0bcbc57447b7b06b3491ba6113f6fc98a8fbdaecdd488a129a9570c1ad7d7b","observation_id":"c668a9ca-be39-4858-af4e-c6f63fdd66c2","resolution":{"observed_at":"2026-07-10T16:37:22.895357Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-10T16:37:23.595079Z","title":"In: Proceedings of the 33rd International Conference on Machine Learning (ICML)","venue":null,"work_id":"029f4942-79b9-439c-8e2e-122f9d93de89","year":2016},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:86a77066e6d62e9f17b0deba22c9304aaee83fb268e43cfbe10edc8aa21c1fd1","observation_id":"b39d9512-294c-42a1-895c-0a4b1dd32f18","resolution":{"observed_at":"2026-07-10T16:37:23.597375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-10T16:37:23.597920Z","title":null,"venue":null,"work_id":"918bb3a7-4947-416e-bed0-8af649176afd","year":2022},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:7d05d3dfec06e9ef802872eb945e94fa41803a34ed66e61da4986349bdab56f3","observation_id":"946694af-1858-4aad-8ea3-1bde93d53818","resolution":{"observed_at":"2026-07-10T16:37:23.599202Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1459.1974","doi":"10.1080/01621459.1974","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Robust tests for the equality of variances","venue":null,"work_id":"daf52b3a-9e55-4b7f-95f7-92d80343b9e9","year":1974},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:de07723048a1f703bbd5d53e51d3a70c198448facbdbb04b0c201ea85e7ddb3f","observation_id":"c4296574-fb97-41b5-b193-3a8a89343fcc","resolution":{"observed_at":"2026-07-10T16:37:22.886181Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1115/1.3225682","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Journal of Engineering Materials and Technology106(1), 88–100 (1984)","venue":"Journal of Engineering Materials and Technology","work_id":"124a7ff8-474a-4a22-befc-8b07da4ec50a","year":1984},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:434ed889e189a4ed1e8bdfb6dd91f35bf344d4d667236e4eef38c24b92c70e05","observation_id":"da61809a-abd0-4b8f-a659-08bc173dd696","resolution":{"observed_at":"2026-07-10T16:37:22.877919Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1115/1.3226448","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Journal of Engineering Materials and Technology 111(2), 154–162 (1989)","venue":"Journal of Engineering Materials and Technology","work_id":"42bbc15a-e613-4c03-af51-089549d5ad8b","year":1989},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:ef590c0586e9718399638d20174ec9cf336a80f80247a517b28dfd8e7db83ec5","observation_id":"eda8ad4b-a4d6-4a1e-a44c-6e717e4c6a6b","resolution":{"observed_at":"2026-07-10T16:37:22.873578Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/ma14144032","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Materials14(14), 4032 (2021)","venue":"Materials","work_id":"6fc77c46-458d-4611-b5f9-111262f2c9bc","year":2021},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:b21edbacbf141c6832cc75152a6f1117fc0396c73f2f0d7929331a4f2b159f55","observation_id":"b2ed446e-7eba-48eb-9fcf-c551453c5594","resolution":{"observed_at":"2026-07-10T16:37:22.879529Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-10T16:37:23.601822Z","title":"The ASQC Basic References in Quality Control: Statistical Techniques (1993)","venue":null,"work_id":"42e5538a-2a8d-4bc6-8b1e-ad072e6eb635","year":1993},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:224a08ddf173fc568601a94df6dc2e2f59b7ccdea024da42969f593d2ea34bbf","observation_id":"27726e90-6481-49fc-947c-5f96174db7d9","resolution":{"observed_at":"2026-07-10T16:37:23.603091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.jesp.2013.03.013","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median , journal =","venue":"Journal of Experimental Social Psychology","work_id":"6c438304-2e15-440c-9bee-1c90df24634d","year":2013},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:f700b7fc613b11ace0fdcf287a11331726d117ca98b169e212d4faeb4645ea32","observation_id":"81562127-4e50-4ee0-9221-6db845193170","resolution":{"observed_at":"2026-07-10T16:37:22.899692Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-17T21:51:20.802045+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-17T21:51:20.802045+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/s1365-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T16:37:22.870464Z","title":"International Journal of Rock Mechanics and Mining Sciences34(1), 17–25 (1997)","venue":null,"work_id":"948b7200-2a0f-4a23-b377-60a32109b032","year":1997},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:22eb25bf8b94874a89eea94f22af2020d8da13233fd46b490451919528477511","observation_id":"1decb978-1945-4b55-830d-8bdd3ddb8d68","resolution":{"observed_at":"2026-07-10T16:37:22.871739Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00170-021-08052-9","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"The International Journal of Advanced Manufacturing Technology119, 503–516 (2021)","venue":"The International Journal of Advanced Manufacturing Technology","work_id":"008ad634-4838-46d4-8fa4-8a51b3a0cf5f","year":2021},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:ae70232af6afa630d0c921cdc7f8c622688ecb9a56eff2158e6fef48199b7145","observation_id":"711ea7d3-15f6-4112-9ef8-70d9a03adfc8","resolution":{"observed_at":"2026-07-10T16:37:22.879686Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.123168","doi":"10.1016/j.eswa.2024.123168","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Expert Systems with Applications246, 123168 (2024)","venue":"Expert Systems with Applications","work_id":"250b2242-a0a8-48e0-847e-d3c0b9f91a02","year":2024},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:f647ebc3f674572f88f3a936b510664b4f6b2c5e96ab148eb44c7e8591f86d9a","observation_id":"40cb398d-64ce-47c8-9576-4f0df9aa2c75","resolution":{"observed_at":"2026-07-10T16:37:22.893213Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.12808","last_updated":"2020-11-11T00:59:17Z","snapshot_observed_at":"2026-07-06T07:18:14.810715Z","submitted_at":"2018-11-13T15:36:42Z","title":"Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning","version":3},"cited_work":{"arxiv_id":"1811.12808","doi":"10.48550/arxiv.1811.12808","metadata_source":"pith","pith_arxiv_id":"1811.12808","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"2018, Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning, https://arxiv.org/abs/1811.12808","venue":"cs.LG","work_id":"ea391993-099a-4edf-833d-eebae54158d0","year":2018},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"cited_paper":"/paper/1811.12808","citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:8e35781c398c82d128b587de2868a4bf2be8906d05beba20a91bd745b50381d2","observation_id":"e301c87a-e81e-4f1c-9f4e-99b506e18373","resolution":{"observed_at":"2026-07-10T16:37:23.059324Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-10T16:37:23.592937Z","title":"I.: Gaussian Processes for Machine Learning","venue":null,"work_id":"db5250a8-979c-4dfd-8f95-a1dea04c270e","year":2006},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:6440b6db2a222f9ed1c0f55eed7107e539846895e06242f53137d0f3a1fe6886","observation_id":"cb7f2af2-6670-4af3-9f11-74af9fe7b43f","resolution":{"observed_at":"2026-07-10T16:37:23.594308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.inffus.2021.11.011","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Information Fusion , author =","venue":"Information Fusion","work_id":"1a552272-cad9-455c-a2f7-3aef14471d4c","year":2022},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:cd5406dc76db3da1549644091841335bd95d4da8b76f0b41398e648b3f3bddac","observation_id":"130e79bf-4700-4aed-a128-f42575920b3f","resolution":{"observed_at":"2026-07-10T16:37:22.897782Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-23T01:53:19.300426+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T01:53:19.300426+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1371/journal.pone.0224365","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PLOS ONE14(11), e0224365 (2019)","venue":"PLoS ONE","work_id":"5bb64e6b-3169-40fd-8315-2d152f12c94d","year":2019},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:697010187e8943cf3ce16f09a6b1e8a936514d7cb2989f0bec36916aa7d4880e","observation_id":"97e00141-6619-4537-8ef3-6b0c09f0717b","resolution":{"observed_at":"2026-07-10T16:37:22.890686Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.neuroimage.2017.06.061","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"NeuroImage , author =","venue":"NeuroImage","work_id":"02f83e7b-3b7f-4c81-a9fd-daa73f45cdb9","year":2018},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:e0eadd3e5c75849a0c09bf8bc48ddf69d4cf847194704c0d5d70e2c5d18cd212","observation_id":"a65ee073-dac5-46a9-ae71-bff46ebd805a","resolution":{"observed_at":"2026-07-10T16:37:22.876160Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-11T17:49:45.192999+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T17:49:45.192999+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3514228","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"2022, ACM Comput","venue":"ACM Computing Surveys","work_id":"65ab419d-e29f-4904-8712-93fefe3556c5","year":2022},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:d4b97992ea935cf34355481518d8991c7aabcb25ad08a2b4d16118fc8c5ae21c","observation_id":"b6504a0f-9bd2-49eb-b3c3-4cc41387f3f0","resolution":{"observed_at":"2026-07-10T16:37:22.897375Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-03T20:38:11.344011+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T20:38:11.344011+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41524-023-01000-z","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T16:37:22.893102Z","title":"Small data machine learning in materials science","venue":"npj Computational Materials","work_id":"29a22655-4758-4a3f-bc62-974e453cc0e7","year":2023},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:b81150ea8ca83dedde4711f9e06bb7515c5cb6059633387a3f2627ef90a5be97","observation_id":"df2b81b4-6428-42ae-b5e7-ac3fdf4f3678","resolution":{"observed_at":"2026-07-10T16:37:22.894336Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.eng.2024.04.024","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi: https://doi.org/10.1016/j.eng.2024.04.024","venue":"Engineering","work_id":"5454b162-026e-4d76-ac79-70b94d2d7c0d","year":2024},"citing_paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-10T16:29:23.355323Z"},"links":{"citing_paper":"/paper/2607.07863"},"observation_digest":"sha256:4d5758f4aed4d94cadaf6d3dbc4fdcfb064f4a0e975e39a1c5b07c953c380c10","observation_id":"35aee649-c465-483b-b3e8-6b92d1a00263","resolution":{"observed_at":"2026-07-10T16:37:22.896293Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.07863","last_updated":"2026-07-08T18:54:09Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T02:10:49.528136Z","submitted_at":"2026-07-08T18:54:09Z","title":"Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":1,"metadata_mismatch":6,"parse_uncertain":0,"unresolved":1,"verified_exact":16,"verified_fuzzy":4},"total_outbound_references":28},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2607.07863."}