{"as_of":"2026-08-10T18:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4f1a29adffe32c7fc2647cdf2e133a1f8381f1aae3094b49d068dde8cc4d47ea","coverage":[{"denominator":81,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":81,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T00:03:50.526962Z","state":"measured"},{"denominator":81,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":81,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2502.03982/citation-record","integrity":"/paper/2502.03982/integrity","json":"/paper/2502.03982/citation-record.json","paper":"/paper/2502.03982"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T00:03:49.816148Z","title":"Drug discovery and development: introduction to the general public and patient groups","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.816148Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:62c4e89fff09506fb02c30be38dd83b2748a5b40445a1160b946e731b93bf79f","observation_id":"90d3412f-a051-44d4-8c1d-fc9ee2e2137b","resolution":{"observed_at":"2026-08-09T00:03:49.816148Z","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":"2026-08-09T00:03:51.316938Z","title":"Innovation crisis in the pharmaceutical industry? a survey","venue":null,"work_id":"9f5388fc-ee90-41a1-836e-6cd5ae3b8242","year":2021},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.819590Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:ded06d75c6205b85de1dcd3819fca08f4a9443520a7280c8b52f3ef7dda551c1","observation_id":"64013af0-7a4e-4716-94a2-5c61da5f3900","resolution":{"observed_at":"2026-08-09T00:03:51.319943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.309212Z","title":"Computational approaches streamlining drug discovery","venue":null,"work_id":"f9df9e15-d015-4cfa-a8db-4ac12cf5982c","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.822781Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:50c551882523440e4f7b8b8ab3bd3457c3c2ca1422b16406c8f4c621616fe376","observation_id":"81158704-cbcb-4570-aa49-548b1ed59d29","resolution":{"observed_at":"2026-08-09T00:03:51.312150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.301293Z","title":"High-Throughput Screening: New Technology for the 21st Century","venue":null,"work_id":"42bbfafb-86e7-4cf7-ae12-9014ddce605b","year":2000},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.825524Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:e4399b3ae3519388fa4f6793b3ffe48827a5a1849145761d063d67cff39eebf3","observation_id":"ab48bbc1-7514-4b0c-92b7-7cd72cddbd76","resolution":{"observed_at":"2026-08-09T00:03:51.304062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.282053Z","title":"Supervised Prediction of Drug–Target Interactions Using Bipartite Local Models","venue":null,"work_id":"2573e601-f595-4762-bcb2-22ded80e083d","year":2009},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.828308Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:c8784d04850068b7157ce558aee653046ddd76be27b71c15fa5c367d99ff8a94","observation_id":"3ff11166-a30b-44b4-9e3a-c3f650328e56","resolution":{"observed_at":"2026-08-09T00:03:51.287132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.272959Z","title":"The Concept of Probability in Safety Assessments of Technological Systems","venue":null,"work_id":"41471d53-defa-49e4-b007-65e10de9ff38","year":1990},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.831074Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:81270f5b22db015cc1005d5c98347b6c54f9094ec4a8b1bb79fb38b3b9f963c1","observation_id":"5fd33852-6c1d-42d7-839e-c204ea4dbecb","resolution":{"observed_at":"2026-08-09T00:03:51.275735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:49.838066Z","title":"Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.838066Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:62837bf2b795efb6f08e82c492ee8898c3f616b737b473563aaf76256f08340a","observation_id":"9692bd99-c5e0-4e08-b8c6-2febf53e8caf","resolution":{"observed_at":"2026-08-09T00:03:49.838066Z","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":"2026-08-09T00:03:51.265469Z","title":"Sources of uncertainty in machine learning – a statisticians’ view, 2023","venue":null,"work_id":"219ad95f-aff8-497f-abb5-bcbf2c262d7a","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.849570Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:35f1a9900b719a392568842d609a8e7c294e3792d659c3b2af0acdf7b4642876","observation_id":"28e1b3f4-cbe5-4943-941c-4a9257f543e3","resolution":{"observed_at":"2026-08-09T00:03:51.268255Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:49.860159Z","title":"Bayesian learning for neural networks, volume 118","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.860159Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:b6c9fb957bb2177f0e2d45537f6e980a9c8eb322bb8dca76c5428bd38d519a53","observation_id":"2e5249fb-d8d7-4e4b-aac3-284d20b6411e","resolution":{"observed_at":"2026-08-09T00:03:49.860159Z","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":"2026-08-09T00:03:49.871131Z","title":"Weight uncertainty in neural network","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.871131Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:01aeac87e166416ed428e450b62c60051db1c395b81327763fcf8545aa8fc5ea","observation_id":"ab7bbf39-5827-4f01-830f-98313b70f1e4","resolution":{"observed_at":"2026-08-09T00:03:49.871131Z","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":"2026-08-09T00:03:51.250581Z","title":"What are bayesian neural network posteriors really like? In International conference on machine learning, pages 4629–4640","venue":null,"work_id":"5125368b-db0a-4a93-846a-2401ee4823d1","year":2021},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.882123Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:10a16daad6f6f74d860895fb5a56f950f138a70a95d4f8188c4f82464b0a3098","observation_id":"4aae49e9-33fd-4c1e-a292-48d7760eb7b8","resolution":{"observed_at":"2026-08-09T00:03:51.253419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.243015Z","title":"Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction","venue":null,"work_id":"8e04d96f-4504-40d9-b155-891cdd5f1297","year":2021},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.893349Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:8ccb7f140799d31c988964ceb3c9b6477707c900fd4bd9f220d47f958e4f9ff4","observation_id":"7034baff-526a-4221-92e5-342f72160bbd","resolution":{"observed_at":"2026-08-09T00:03:51.245900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.235544Z","title":"Simple and scalable predictive uncer- tainty estimation using deep ensembles","venue":null,"work_id":"149f81bf-05b1-4fa8-9bc8-eaf3b99f8564","year":2017},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.905501Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:66b3ac435891b6d68e4cfad40f90e7f7d5ce3a2d00e41c5c04935d3b03cd7de5","observation_id":"85cc98e9-d867-4e1e-818e-f83247455e55","resolution":{"observed_at":"2026-08-09T00:03:51.238448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.228151Z","title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","venue":null,"work_id":"a404d555-e0d8-4ef7-859f-eeb9a039183d","year":2016},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.916564Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:94b47aefde1301f16122bc811fcc87dc519681235c824271f93eb05f14555329","observation_id":"eeaaac33-853c-47cd-a590-a934e5fcff00","resolution":{"observed_at":"2026-08-09T00:03:51.230910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.07511","last_updated":"2022-12-07T05:08:01Z","snapshot_observed_at":"2026-07-06T11:29:34.579530Z","submitted_at":"2021-07-15T17:59:50Z","title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.07511","snapshot_observed_at":"2026-08-09T00:03:49.922148Z","title":"A gentle introduction to conformal prediction and distribution-free uncertainty quantification","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.922148Z"},"links":{"cited_paper":"/paper/2107.07511","citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:047929c4675f3fce51c43b558facc538f8f0ed16a573552d64c7e0bf6c2252f5","observation_id":"afb4ca92-58fe-436c-ad88-9ea483b77ded","resolution":{"observed_at":"2026-08-09T00:03:49.922148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12274","last_updated":"2022-07-25T15:44:19Z","snapshot_observed_at":"2026-08-07T00:43:10.385651Z","submitted_at":"2022-07-25T15:44:19Z","title":"MAPIE: an open-source library for distribution-free uncertainty quantification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12274","snapshot_observed_at":"2026-08-09T00:03:49.931900Z","title":"Mapie: an open-source library for distribution-free uncertainty quantification","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.931900Z"},"links":{"cited_paper":"/paper/2207.12274","citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:83a1cd4c6024badac3cbc6f5b040c56ee22d7ecf5be5fbeea40cf0867a71f49c","observation_id":"a12e7ffd-3641-4294-901e-ecd60c647b40","resolution":{"observed_at":"2026-08-09T00:03:49.931900Z","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":"2026-08-09T00:03:49.943003Z","title":"Evidential deep learning to quantify classification uncertainty","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.943003Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:fbd834fc1d6355bbb85756a4ba476553fe4f047793a474ce5b96e0ac8767eeb6","observation_id":"b306222c-06f2-4ec2-9a4d-9f02bc42f2f2","resolution":{"observed_at":"2026-08-09T00:03:49.943003Z","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":"2026-08-09T00:03:51.217149Z","title":"An uncertainty- guided deep learning method facilitates rapid screening of cyp3a4 inhibitors","venue":null,"work_id":"316e2800-bddc-4560-a7c2-d71a3285f1e2","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.953835Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:03a7670006aa3fc8fafa89dcbe51dd4c1b615754a6f02c16c95104ddc858d1f8","observation_id":"b5804ac1-cfee-46b4-8756-3dda183f33c8","resolution":{"observed_at":"2026-08-09T00:03:51.219738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.209581Z","title":"Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1a (kdm1a/lsd1) inhibitors","venue":null,"work_id":"27c1a348-849b-4a1c-a89c-a566dc80115f","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.964810Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:50c25f5f939fa530e19bc850e9e69ba6ec20b37702b98f5703a7ff4dd10e0e49","observation_id":"d9a1f338-37ed-45c7-8b5b-e3cff6d26671","resolution":{"observed_at":"2026-08-09T00:03:51.212548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.201626Z","title":"Improving evidential deep learning via multi-task learning","venue":null,"work_id":"dc62e108-e017-4951-b04e-cf84c3540a6f","year":2022},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.975854Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:f6aafce2b30622da232ec58a6dc892a5646f2e76bfdd93bf3dca5d11294095ed","observation_id":"31e91640-250b-4f66-bbce-8ef6e2536755","resolution":{"observed_at":"2026-08-09T00:03:51.204517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.194268Z","title":"Evidential deep learning for guided molecular property prediction and discovery","venue":null,"work_id":"27509f42-7c31-434b-a2bd-77a287fa34bb","year":2021},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:49.986831Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:87faa4bb7e9e4fc0064d774c6aa134268edd117a183daeacf39a101eb547e7a2","observation_id":"1ca03744-cbc6-435e-abe1-41ecc54b1329","resolution":{"observed_at":"2026-08-09T00:03:51.197296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.186652Z","title":"Simple and principled uncertainty estimation with deterministic deep learning via distance awareness","venue":null,"work_id":"124f65e2-e533-4839-9fc1-234b8f035741","year":2020},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.010603Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:933556526c3df58837c583daf76541baae08f765878358c07c97413c2d9b9a55","observation_id":"f61f68e6-5aa9-437a-8a65-73fe5d664730","resolution":{"observed_at":"2026-08-09T00:03:51.189567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.179148Z","title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","venue":null,"work_id":"599ea2b0-dedb-4a53-bd6f-676d4cb32b39","year":1999},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.021433Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:c0ea696957f72e2c402fb4febc8c664ecafc3b72c72f8164bfd94f542460aa8e","observation_id":"55b3fabe-9604-48e3-9ec5-61b281021e9e","resolution":{"observed_at":"2026-08-09T00:03:51.181891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.171364Z","title":"Venn-abers predictors, 2014","venue":null,"work_id":"ca75372c-cdde-457c-b112-02e250f6e2c8","year":2014},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.037300Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:91331d8a7d1d40e688b6e6c96e3d55c5f841ab568f18ea0189e74c2d7f2aae97","observation_id":"7637f1fa-1f44-4ba8-b1c1-0f083b919634","resolution":{"observed_at":"2026-08-09T00:03:51.174480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.049949Z","title":"Transforming classifier scores into accurate multiclass probability estimates","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.049949Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:764eefaaf28bcc2950db8ee1590d8ae1f3dfe71b4b92a5abf6413a86eca5236e","observation_id":"cd4f2194-d040-4ae3-a4b6-4d75dc13eb1d","resolution":{"observed_at":"2026-08-09T00:03:50.049949Z","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":"10.1016/j.drudis.2020.11.027","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Mervin, Simon Johansson, Elizaveta Semenova, Kathryn A","venue":"Drug Discovery Today","work_id":"961ee4fb-e8f7-47e3-ada1-6f8af7d8e85a","year":2021},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.060914Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:2acbe58cee5280d76ed45a4c23ffce8dfeb03a0b678f43c9b462bfb8d34c3fc1","observation_id":"c1abc614-a403-4bd2-9d15-7d9542abfb32","resolution":{"observed_at":"2026-08-09T00:03:50.608984Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.159862Z","title":"Uncertainty quantification: Can we trust artificial intelligence in drug discovery? Iscience, 25(8), 2022","venue":null,"work_id":"98739baf-3717-4dae-9bf4-ca99fcf436c4","year":2022},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.077168Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:e2450b2b92b6f0bfdb08bcfe614b66684b183e88915d0f165ab50d24933832ec","observation_id":"34f9a7ad-bd9d-4d8e-b6d3-7ab023a1714d","resolution":{"observed_at":"2026-08-09T00:03:51.162646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.152721Z","title":"A large-scale study of probabilistic calibration in neural network regression","venue":null,"work_id":"51611c46-bd1b-47e5-9b46-0e4ad137bf15","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.080091Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:3da0da6605752deeace1f9891253ab6ddea7cb826bacbc0946cf07cdf3d4b217","observation_id":"5d55850a-439f-4410-b054-972328225cd4","resolution":{"observed_at":"2026-08-09T00:03:51.155462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.144734Z","title":"Quantifica- tion of uncertainty with adversarial models.Advances in Neural Information Processing Systems, 36:19446–19484, 2023","venue":null,"work_id":"e1f1be75-788a-45fa-88c1-d7e1d7faded8","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.082608Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:59085f1aee5dcaff2383b7c4a0bc884690d5034890cff45669da256915ebdde0","observation_id":"611401a1-bfd8-4ffb-bdae-b5ad7b7fc40d","resolution":{"observed_at":"2026-08-09T00:03:51.148098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.132585Z","title":"Probabilistic random forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty","venue":null,"work_id":"4394157c-3957-475c-9758-f62758bb006c","year":2021},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.085084Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:aba372c0c689b3d6222ed2e28adff3b3a11abe602f080078ee17ace6cad98aff","observation_id":"6d02e0c5-aa59-423a-a981-fe8501ee3b8c","resolution":{"observed_at":"2026-08-09T00:03:51.137295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.118359Z","title":"An ensemble-based approach to estimate confidence of predicted protein–ligand binding affinity values","venue":null,"work_id":"9e2ca2f3-b47c-49e4-bfc6-592180d54b8b","year":2024},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.087708Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:9d347164bffbac238f91d4bb0eda48de815008b353b92836c87b4c20f4cad9d7","observation_id":"0be0b087-9d0e-49a7-aca6-33998a98756a","resolution":{"observed_at":"2026-08-09T00:03:51.126940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.098527Z","title":"Reducing overconfident errors in molecular property classification using posterior network","venue":null,"work_id":"e83212c8-cbd7-4493-ac2a-0f4f0460f3cc","year":2024},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.090229Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:585d62869b0110351aaae02192b0dfc5e40cfe282285e43a2d4b3aab3f09e62b","observation_id":"3ac96f55-0101-41f4-a9fe-82c3666120d0","resolution":{"observed_at":"2026-08-09T00:03:51.112411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14185","last_updated":"2024-07-19T10:29:00Z","snapshot_observed_at":"2026-07-06T18:48:58.038753Z","submitted_at":"2024-07-19T10:29:00Z","title":"Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models","version":1},"cited_work":{"arxiv_id":"2407.14185","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.14185","snapshot_observed_at":"2026-08-09T00:03:50.630009Z","title":"Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models","venue":"cs.LG","work_id":"79666436-becf-4786-8000-b58b03ef5b6d","year":2024},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.092878Z"},"links":{"cited_paper":"/paper/2407.14185","citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:0b062c963aca3ea6ba89b1cbf7770731d128c6799f1e5208921faf4b8d40e26a","observation_id":"7fd533d1-e182-4e4a-bfd3-715ed3abadee","resolution":{"observed_at":"2026-08-09T00:03:50.633121Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.081416Z","title":"Uncertainty quantification using neural networks for molecular property prediction","venue":null,"work_id":"83e5aa34-d5e0-4b00-8bae-9675f0337b4f","year":2020},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.095931Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:ae62be7e7f13ccdc977ec03374be29f713c8762d8008728f2511c784495bed6e","observation_id":"1376a3a1-0a35-4cb6-93dc-ed29750eb81f","resolution":{"observed_at":"2026-08-09T00:03:51.084309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1021/acs.jcim.0c00476","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Mervin, Avid M","venue":"Journal of Chemical Information and Modeling","work_id":"c38f2972-d319-4010-8d17-54f172b94641","year":2020},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.098296Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:da3f5c4d03a45a6ae3ff3ec064136a7a3e24ffc10a6a37b3fe5faf4adcb540bc","observation_id":"32566fbc-f930-4c21-835d-92d3c0e68551","resolution":{"observed_at":"2026-08-09T00:03:50.600980Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.073569Z","title":"Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimation","venue":null,"work_id":"07a55061-d90a-4eb9-bccb-96a68bc723fe","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.106810Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:9216e953aacb5be7d75ee0039d0e4be6d1ba51b16edde6ddeffa84669e41fd9a","observation_id":"43eb7789-4be3-4f61-8c91-7eea2ef6ec6f","resolution":{"observed_at":"2026-08-09T00:03:51.076382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.118179Z","title":"Time-split cross-validation as a method for estimating the goodness of prospective prediction","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.118179Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:1d9facc3762475b68a7b9ce50909476ddb0abf3affa8de2294bac7acc1202da4","observation_id":"bfe70b5e-ab2a-47fe-96e4-7bd45b269e89","resolution":{"observed_at":"2026-08-09T00:03:50.118179Z","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":"2026-08-09T00:03:51.065982Z","title":"Simpd: an algorithm for generating simulated time splits for validating machine learning approaches","venue":null,"work_id":"d0c52369-0979-46f5-8230-8d6929b73e7c","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.129589Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:dc7adc8c43ef13a94a73b62de3c87f5aaf430c02a59d5a1be7b28f49ad961df2","observation_id":"a73af5f8-a064-469c-b4e5-71cdc11011dc","resolution":{"observed_at":"2026-08-09T00:03:51.068846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.058248Z","title":"Learning classifiers when the training data is not iid","venue":null,"work_id":"5345255d-d525-4fef-8813-4a64cf183f6e","year":2007},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.135076Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:edffecc2e843e4e1c32464a8e66de871c1fae6f2840209a5f6e9fadb15e41c31","observation_id":"45e08393-004e-4a30-ba86-6a590502ddb4","resolution":{"observed_at":"2026-08-09T00:03:51.061270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.050683Z","title":"Beyond iid: Non-iid thinking, informatics, and learning","venue":null,"work_id":"b8e4096a-01b4-4a7e-a042-c3c22e2a32cf","year":2022},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.146389Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:995ddb43ed6fae61cb21cad7bffb079eba19d0834d66c473e93903b903ede14d","observation_id":"871d00b7-2cbc-4ebc-a798-83a9dd143f93","resolution":{"observed_at":"2026-08-09T00:03:51.053583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.043185Z","title":"Sculley, Sebastian Nowozin, Joshua Dillon, Bal- aji Lakshminarayanan, and Jasper Snoek","venue":null,"work_id":"4093d3a6-400c-4aec-a4b5-507ee1cff935","year":2019},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.159517Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:b7d126fa6fec92fee3b8fff45f5a42019b475deafcf988293bf3a80ca7b35f80","observation_id":"9e79dac0-344b-4419-947d-b0322adeae5d","resolution":{"observed_at":"2026-08-09T00:03:51.045953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.178731Z","title":"Wilds: A benchmark of in-the-wild distribution shifts","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.178731Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:7d46d03f17ef4478c1a86bdc18ba9d03401dd77b6fa67607ffc28ca7d50f22a7","observation_id":"b1f778fc-722a-469d-9f76-c8dda745b4fd","resolution":{"observed_at":"2026-08-09T00:03:50.178731Z","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":"2026-08-09T00:03:51.031754Z","title":"Weinberger","venue":null,"work_id":"f8d4e73b-e18e-46dc-8b1f-83e907c22572","year":2017},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.192492Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:22e1bf005c1a4a6356798a65bbfdb68a5f5c4ee766ffd47c0ad07caf635cc8c5","observation_id":"a30ad299-4c83-423d-831a-2f964c532ef5","resolution":{"observed_at":"2026-08-09T00:03:51.034505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.023934Z","title":"Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest","venue":null,"work_id":"6894a95c-26cb-4ea6-bba6-a1bfa78b67b3","year":2012},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.217094Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:549a6509767b18d4b1ca998ed521c83c6ce25b5cffbf159a11c051716b2a840f","observation_id":"71ccf4e2-f910-48e3-9daf-96d98d640255","resolution":{"observed_at":"2026-08-09T00:03:51.026814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.230014Z","title":"Predicting good probabilities with supervised learning","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.230014Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:8fc3a23ab40084e2c8efaec19186807e7fd4e979269e86ac689aca50310caef9","observation_id":"b66ab7ac-4da9-40fb-99fb-d84281c22fde","resolution":{"observed_at":"2026-08-09T00:03:50.230014Z","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":"2026-08-09T00:03:51.012467Z","title":"The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic acids research, 52(D1):D1180–D1192, 2024","venue":null,"work_id":"cdf5250f-e014-4341-8fb4-529da663a323","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.247810Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:dd68cb576bc8c4dccbebb78a088daf10174b7831293e2f1a24d6b69e07a3139e","observation_id":"c840834b-e243-40fe-b768-94f740b5ab46","resolution":{"observed_at":"2026-08-09T00:03:51.015377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:51.005255Z","title":"Multispecies machine learning predictions of in vitro intrinsic clearance with uncertainty quantification analyses","venue":null,"work_id":"60932c66-2c25-47f6-8365-e14c73668f99","year":2022},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.255281Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:d26982d25c8d180a6b19e3c8f5958bcd182a676aa0001958acaeac71c07edd44","observation_id":"644321fb-29b6-4ba2-be34-9d68d8d8e292","resolution":{"observed_at":"2026-08-09T00:03:51.008071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.997673Z","title":"Computational predictions of nonclinical pharmacokinetics at the drug design stage","venue":null,"work_id":"e4efd069-17d8-474e-9fac-89baf59e2ee6","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.264504Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:3156a87438e55cb805009817c1c67189d883eb7c6d6fd960832e2153a0280e3e","observation_id":"eda7c1b8-0480-4602-b9f6-4839acba6f76","resolution":{"observed_at":"2026-08-09T00:03:51.000636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.04313","last_updated":"2024-09-06T14:38:47Z","snapshot_observed_at":"2026-08-04T08:49:27.129730Z","submitted_at":"2024-09-06T14:38:47Z","title":"Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels","version":1},"cited_work":{"arxiv_id":"2409.04313","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.04313","snapshot_observed_at":"2026-08-09T00:03:50.619825Z","title":"Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels","venue":"cs.LG","work_id":"dd56e180-653a-41dc-ba0d-8ef4b3a3c322","year":2024},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.273397Z"},"links":{"cited_paper":"/paper/2409.04313","citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:8e44c750145c57863f71e4974f5c27674c03fbb7e87331585dd76259ceb623c0","observation_id":"deefeaad-7d97-4aab-881d-2c8c84d5b856","resolution":{"observed_at":"2026-08-09T00:03:50.622694Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.983729Z","title":"Towards reliable uncertainty estimates for drug discovery: A large-scale temporal study of probability calibration","venue":null,"work_id":"a67929fa-3362-4af7-b61a-abbf0263b200","year":2024},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.280693Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:7b453de6ff1deebf584b1ad22dd640f0edc279b286874a718cb0c6e49c631a0a","observation_id":"997b7073-7e9f-4b34-be13-1a1af86b30f4","resolution":{"observed_at":"2026-08-09T00:03:50.993105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.961939Z","title":"Temporal evaluation of probability calibration with experimental errors","venue":null,"work_id":"8077de69-14bf-4803-8adb-6a7e6077313f","year":2024},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.283302Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:a8ae68d5cf0211164bdd46ef829458c0072a645d040e91632ef9087fbf663a92","observation_id":"ab7e92e6-c54a-49ec-a365-f3c1d5adcc22","resolution":{"observed_at":"2026-08-09T00:03:50.971376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.939814Z","title":"Risks in new drug development: approval success rates for investigational drugs","venue":null,"work_id":"c6eafdc4-0c46-4ca4-b518-f4b067f7a510","year":2001},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.286068Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:148350084a796ea4956c0800e51ea6fc1c3b4f1c55e9eab114cdbeab020a59dc","observation_id":"5da8a1c1-b435-46d4-811d-b72a8122ddb0","resolution":{"observed_at":"2026-08-09T00:03:50.949209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.917926Z","title":"Admet in silico modelling: towards prediction paradise? Nature reviews Drug discovery, 2(3):192–204, 2003","venue":null,"work_id":"3dc06522-d404-46f4-a57f-3b01064f4099","year":2003},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.288882Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:e74476b2b8f0536c187331568283f3f6cfcd92a1b39d1a2a5777c5f5944739cb","observation_id":"a0267ba8-39bf-4844-9037-abb6531e291e","resolution":{"observed_at":"2026-08-09T00:03:50.926852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.903509Z","title":"Mechanisms of cyp450 inhibition: understanding drug-drug interactions due to mechanism- based inhibition in clinical practice","venue":null,"work_id":"acde0f38-d700-4dbf-8efb-8c88be1f1370","year":2020},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.291689Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:f18930cfd2e1b1f28fef8f36886e13aec4533b14c0cff0c7b21f5503dd53006d","observation_id":"54c6d5cc-35e3-4ee3-9a52-4acc290361d0","resolution":{"observed_at":"2026-08-09T00:03:50.906477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.896169Z","title":"Cytochromes P450: metabolic and toxicological aspects","venue":null,"work_id":"6f336de0-1ca8-464b-8938-f540d97e3970","year":1996},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.294175Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:7944ef4cc81d3da76c9a7d0623b7c440dba74143dec97dbca7192467e9d87c1c","observation_id":"f02b1abf-ad56-4091-93a6-21a0f44d4f3f","resolution":{"observed_at":"2026-08-09T00:03:50.898998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.888528Z","title":"Cytochrome p450 enzymes in drug metabolism and chemical toxicology: An introduction","venue":null,"work_id":"2e4bf4ab-05f7-4f00-8fb2-b391a33e46eb","year":2006},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.296619Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:7f4421acb5c362a92ec0dc94d70321eabcf3f9340a288f7380592e08f9390ca1","observation_id":"f02e5663-940f-4e03-97db-8093c6097d35","resolution":{"observed_at":"2026-08-09T00:03:50.891478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.880718Z","title":"Role of caco-2 cell monolayers in prediction of intestinal drug absorption","venue":null,"work_id":"e1585e2d-fce6-4d74-ae7f-79ff6a3e9958","year":2006},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.299240Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:4724e9d6bcae65fcfcfda803fa02ae956f73088206740e16cea54f62a751e02c","observation_id":"d1ba5ae0-b4ec-4afb-89d3-e6b960a021e2","resolution":{"observed_at":"2026-08-09T00:03:50.883685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.873303Z","title":"Bridging solubility between drug discovery and development","venue":null,"work_id":"e36136ba-284e-46d7-a9cc-5915609658e8","year":2012},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.301539Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:b01c5439205fdd4bc07938c7a7fa104c1bd6030fce43aaf857cc5e5cb3082494","observation_id":"341a1bee-de9d-462c-a4ea-400659808fa0","resolution":{"observed_at":"2026-08-09T00:03:50.876057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.865495Z","title":"Molecular genetic insights into cardiovascular disease","venue":null,"work_id":"1ff895f1-539e-45b2-a240-08e3a28c40d0","year":1996},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.303950Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:4679e56ea814819946340efccbe6eb26d0bc760aaa9098ce02c57529a2b72047","observation_id":"f5398115-ab4e-4b57-8cf5-501ca870e0a4","resolution":{"observed_at":"2026-08-09T00:03:50.868618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.855773Z","title":"Lipophilicity in drug discovery","venue":null,"work_id":"9ecd79a9-f107-4dcc-9271-9a39098390eb","year":2010},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.306612Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:9a98f8554aeb7a6d465a9c69bfcbe7ce59a2235b326d70c04e909a771bb6340e","observation_id":"6e7a9742-d470-4242-a876-42cbd56cdcae","resolution":{"observed_at":"2026-08-09T00:03:50.860170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.847801Z","title":"High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury","venue":null,"work_id":"cebd344f-a9db-4b9f-8a9d-1d7128219d4c","year":2013},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.309209Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:e22864f1645bda5729a7786ef11f99dcba8ac87b7835f7b67963c65f8d87b79b","observation_id":"376a0740-89a2-4d25-bd62-b69266f4549b","resolution":{"observed_at":"2026-08-09T00:03:50.850675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.839759Z","title":"In vitro high throughput screening of compounds for favorable metabolic properties in drug discovery","venue":null,"work_id":"9e6a6cce-478b-402c-8c37-5056adb8f68c","year":2001},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.311840Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:69bb5a010459c2e5fe3c940d0f516361b23b79c38ca5fa44341d5fc8b6881e62","observation_id":"6f404ec8-55f6-4433-a1bb-81d6c4a6008a","resolution":{"observed_at":"2026-08-09T00:03:50.842605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.832042Z","title":"Optimization of a higher throughput microsomal stability screening assay for profiling drug discovery candidates","venue":null,"work_id":"f07b6782-4f0b-4601-9da9-c2be2ff26aa2","year":2003},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.329480Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:16038f6864b8b4b073a4d7c3887710c1c2aa9f597cca4fa9ae886437251091b4","observation_id":"976cfd2d-1278-455d-9c58-c1fcc8bf394a","resolution":{"observed_at":"2026-08-09T00:03:50.834998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.824178Z","title":"Rdkit: Open-source cheminformatics, 2006","venue":null,"work_id":"e733cf38-1852-43c0-9c50-d95e8e27aa39","year":2006},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.355928Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:62e36adc23343358e2464e2988fac98c7d6af4dedcd21691df57e25b7df80d92","observation_id":"248fee82-6644-4725-a37f-64bad2998b21","resolution":{"observed_at":"2026-08-09T00:03:50.827224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.801206Z","title":"SMILES, a Chemical Language and Information System","venue":null,"work_id":"9e588b66-306c-461e-a84f-24713ff0a2c2","year":1988},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.370484Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:b04a20a67896397ebeef5f2e25d39a073842f830d9ccf455643dddc31b63e092","observation_id":"f0c3e133-02c7-48b6-8ac3-ebe84354e793","resolution":{"observed_at":"2026-08-09T00:03:50.811213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-031-72381-0_9","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Registries in Machine Learning-Based Drug Discovery: A Shortcut to Code Reuse","venue":"Lecture notes in computer science","work_id":"f8418cb1-59af-4ba1-8762-df9ae544506f","year":2024},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.400907Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:0cf4fa86ff638dbe1060d188a2b2a1a5547155e5b0dbef042ff4c61211a051b0","observation_id":"39d75715-fcc7-4e8f-b0d8-a552d2d275e3","resolution":{"observed_at":"2026-08-09T00:03:50.570847Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.422617Z","title":"Pedregosa, G","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.422617Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:72c718018bf0de0b64cbcd0c854a36bbde97731b8d31da4aac950eca5d6e6245","observation_id":"ddd2de04-43c3-4777-89ca-af37193855eb","resolution":{"observed_at":"2026-08-09T00:03:50.422617Z","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":"2026-08-09T00:03:50.769662Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","venue":null,"work_id":"94e399b0-f05b-4ded-b2df-de547c25b1b0","year":2019},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.441845Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:74c2ad5c8e9cfc2612e43b03a8078545b47c1a6525ea5524a0ce4aea26fc854d","observation_id":"4c018882-cb9b-4850-896a-ad36d0bd3be4","resolution":{"observed_at":"2026-08-09T00:03:50.783689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.746338Z","title":"Adam: A Method for Stochastic Optimization","venue":null,"work_id":"98d665b5-40b9-46c8-b5a1-9fc51a76db6f","year":2015},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.461296Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:5f56ba3eb97fd4d01d0b038427c8104c3bc885f583b9c5e227c860e8a1a70267","observation_id":"da8a5503-be49-482b-9681-c2da3ecbdb7d","resolution":{"observed_at":"2026-08-09T00:03:50.756520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.480529Z","title":"On information and sufficiency","venue":null,"work_id":null,"year":1951},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.480529Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:9afeb330cf91c1cba67c3a037b21f200f349a601ea5d541dee8e6dc2e3d49ed8","observation_id":"22f8f96a-38d9-4096-abd4-efd7bc5111ca","resolution":{"observed_at":"2026-08-09T00:03:50.480529Z","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":"2026-08-09T00:03:50.720779Z","title":"Excape wp1-probabilistic prediction, 2016","venue":null,"work_id":"ef129a72-528b-453a-a56f-f3c93d74aaff","year":2016},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.499917Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:436ff41a72df266f935fe522a7fe6fbe18f078c077407386f1ace4ec82a6da27","observation_id":"3e5fe3dc-c853-40b5-82c0-b25110726ac0","resolution":{"observed_at":"2026-08-09T00:03:50.730756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.502846Z","title":"A kernel two-sample test","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.502846Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:d289010c60b42713aa521db1a0f7575b11bd6091afc59e5c6583ff195f841435","observation_id":"afdb6d08-dd69-4adb-ab6d-e6a36c6f8913","resolution":{"observed_at":"2026-08-09T00:03:50.502846Z","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":"2026-08-09T00:03:50.698349Z","title":"Grouping of coefficients for the calculation of inter-molecular similarity and dissimilarity using 2d fragment bit-strings","venue":null,"work_id":"16a65d2f-88b3-41bc-8dd2-a667bc39661b","year":2002},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.505636Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:2cd4f6adaf24f57265a23178e059647e97b576dc97063445000f67cfdec574a2","observation_id":"76a23383-1ff6-4ed2-85fe-23e62ad5c07f","resolution":{"observed_at":"2026-08-09T00:03:50.701824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.690082Z","title":"The meaning and use of the area under a receiver operating characteristic (roc) curve","venue":null,"work_id":"99351834-6e23-44ea-bbaa-728ff500088f","year":1982},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.508307Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:e620630115afc24cbe58928875f3ada8d7382d6f38b19aaa14db4d7295ce4561","observation_id":"5bcc3644-edf5-49a8-8a13-69819a35fbd0","resolution":{"observed_at":"2026-08-09T00:03:50.693256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.01685","last_updated":"2020-08-07T18:34:21Z","snapshot_observed_at":"2026-08-04T20:23:20.720077Z","submitted_at":"2019-04-02T22:10:44Z","title":"Measuring Calibration in Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.01685","snapshot_observed_at":"2026-08-09T00:03:50.510855Z","title":"Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.510855Z"},"links":{"cited_paper":"/paper/1904.01685","citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:d0317ba2ac23fb4059535d6d65f2d9c192cab966c0e6a4af69de7652a8e7f50b","observation_id":"b80223f6-5d22-4221-a030-5158c094926a","resolution":{"observed_at":"2026-08-09T00:03:50.510855Z","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":"2026-08-09T00:03:50.514078Z","title":"Strictly proper scoring rules, prediction, and estimation","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.514078Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:9c6e0ae20b24bb21f4ef7be0bd9004866b1ce27c6139b54c05896815f7389043","observation_id":"8c402abe-b91a-486f-be07-5e26391aadde","resolution":{"observed_at":"2026-08-09T00:03:50.514078Z","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":"2026-08-09T00:03:50.681738Z","title":"Reliability, sufficiency, and the decomposition of proper scores","venue":null,"work_id":"cb33fdd5-f1e8-44a5-97cb-ddbd8088de89","year":2009},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.516649Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:f74ba64b546b706f3947158bee78f87f08c393fff6e2c97db8a334f458d44b90","observation_id":"200a38b2-cb14-4c8e-a726-11320dfd83f3","resolution":{"observed_at":"2026-08-09T00:03:50.685028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.519157Z","title":"A unified view of label shift estimation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.519157Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:3c9ee1a7f1ac1ca1efdd2cdab5a5a9f033c0d289b35ac80bd6eeaf72ab6454a6","observation_id":"3a0a8bc1-76b0-427f-a77f-96c2230cb127","resolution":{"observed_at":"2026-08-09T00:03:50.519157Z","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":"2026-08-09T00:03:50.668229Z","title":"Evaluating scalable bayesian deep learning methods for robust computer vision","venue":null,"work_id":"f6dee1c7-d787-4df3-a242-bc9f4a185255","year":2020},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.521719Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:fbeae3d8f9e88bec4f5eaeceb6968f95255a970a86a79ca2c82aca5536e9d146","observation_id":"8e8a9976-f351-44fa-a077-700022f91de8","resolution":{"observed_at":"2026-08-09T00:03:50.671323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.659373Z","title":"Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise","venue":null,"work_id":"2e8aa70d-08f6-4f34-b3f5-76f8ab38858d","year":2023},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.524504Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:712c1dc81e8a6cb8966f730a0a8b21af8c4d0c6ab3ea9936c385266319c3b869","observation_id":"fa207645-aa99-475e-96d2-2a58c79b6c6e","resolution":{"observed_at":"2026-08-09T00:03:50.662955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T00:03:50.651322Z","title":"Uncertainty quantification and deep ensembles","venue":null,"work_id":"efeb544e-000c-4246-8ce4-771e3c39509d","year":2021},"citing_paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-09T00:03:50.526962Z"},"links":{"citing_paper":"/paper/2502.03982"},"observation_digest":"sha256:88ecc6fd573b50e3a7c883c3cdb8f984a0ee265fc10c0d8c505f3995b909a657","observation_id":"29421ce4-3d1d-4a04-8965-4fbe862862fb","resolution":{"observed_at":"2026-08-09T00:03:50.653806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.03982","last_updated":"2025-02-06T11:26:04Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T23:55:11.039725Z","submitted_at":"2025-02-06T11:26:04Z","title":"Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models"},"reference_resolution":{"displayed":81,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":5,"verified_fuzzy":59},"total_outbound_references":81},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 0 inbound Pith citation observations for arXiv:2502.03982."}