{"as_of":"2026-08-18T21:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:478faf7b58a1efd1b0429b17810cc28c56516959d883e3af11ff85d209a691b7","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:07:40.723311Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-24T04:23:52.826832Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-08-14T15:39:39.294815Z","title":"A ll Models are W rong but many are Useful: Variable Importance for Black-Box, Proprietar y, or Misspecified Prediction Models, using Model Class Reliance","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.00735","last_updated":"2020-01-27T09:47:49Z","snapshot_observed_at":"2026-08-16T16:54:43.701637Z","submitted_at":"2019-08-02T07:51:37Z","title":"Efficient computation of counterfactual explanations of LVQ models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T15:39:39.294815Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/1908.00735"},"observation_digest":"sha256:7c117471a276cd3df3a2d5ad85e6a2bbf1220e821861ca4bb6fce16077f8f10a","observation_id":"3f2dd07f-a24c-4187-9768-059da46f0eb5","resolution":{"observed_at":"2026-08-14T15:39:39.294815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-08-14T11:09:33.573947Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-18T17:06:34.492277Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.573947Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:91858d4fb28781c310b454c5a3cad84db6c4d07ade04278dd1b37c988a3c1687","observation_id":"73ecad94-60de-4aca-af1c-186b55b4a364","resolution":{"observed_at":"2026-08-14T11:09:33.573947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":"1801.01489","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"0c26fd5a-6dcb-4e57-bf77-3ac71d366e5d","year":2019},"citing_paper":{"arxiv_id":"2402.16391","last_updated":"2026-04-28T07:39:39Z","snapshot_observed_at":"2026-08-01T03:48:12.462157Z","submitted_at":"2024-02-26T08:31:45Z","title":"Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions","version":4},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-24T04:21:49.775278Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/2402.16391"},"observation_digest":"sha256:d910aa0690ca26907abe5651c796a24579f254b40944b465cea2c995b23c6335","observation_id":"242b2578-f061-4583-af33-c3eb37f03ca0","resolution":{"observed_at":"2026-05-24T04:23:52.829193Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-08-12T20:24:47.041271Z","title":"https://doi.org/10.48550/ARXIV","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10490","last_updated":"2024-11-14T18:50:41Z","snapshot_observed_at":"2026-08-16T14:59:43.578278Z","submitted_at":"2024-11-14T18:50:41Z","title":"AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T20:24:47.041271Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/2411.10490"},"observation_digest":"sha256:117ea79a8ea0ad7f7bc9bffd67ef665d354ed0c554ac7dab998cbebbd9809e27","observation_id":"247cdf17-f240-45ad-9cf9-9e9254133885","resolution":{"observed_at":"2026-08-12T20:24:47.041271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-08-10T22:19:44.575672Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.02111","last_updated":"2025-01-03T21:34:35Z","snapshot_observed_at":"2026-08-16T10:16:33.936540Z","submitted_at":"2025-01-03T21:34:35Z","title":"How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T22:19:44.575672Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/2501.02111"},"observation_digest":"sha256:97b15f28c79ea049aba5af98b799a40f9f4d40ca5044bcfbe58edff26be3246c","observation_id":"29ff2fed-863f-4311-8baf-d6a27e77832c","resolution":{"observed_at":"2026-08-10T22:19:44.575672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-08-16T00:07:40.723311Z","title":"2018, https://ui.adsabs.harvard.edu/abs/2018arXiv180101489F http://dx.doi.org/10.48550/arXiv.1801.01489 blue arXiv e-prints , arXiv:1801.01489","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.03023","last_updated":"2025-05-05T20:48:08Z","snapshot_observed_at":"2026-08-18T15:47:41.840506Z","submitted_at":"2025-05-05T20:48:08Z","title":"Automatic detection of Ellerman bombs using Deep Learning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-16T00:07:40.723311Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/2505.03023"},"observation_digest":"sha256:eaac27015c9e8de2a20d6455cef1b42534d386cbf714fbd58b203fd4e7c9253e","observation_id":"568ed3e5-b21f-45f3-8ed5-81a566c5ac5f","resolution":{"observed_at":"2026-08-16T00:07:40.723311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-08-15T22:29:34.851961Z","title":"Model class reliance: Variable importance measures for any machine learning model class, fr om the ‘rashomon’ perspective","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.07005","last_updated":"2025-05-11T15:01:12Z","snapshot_observed_at":"2026-08-18T17:18:43.276063Z","submitted_at":"2025-05-11T15:01:12Z","title":"Explainable AI the Latest Advancements and New Trends","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-15T22:29:34.851961Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/2505.07005"},"observation_digest":"sha256:391001d2813ba74e7eb52576450e7ad52bb5766df8e740dad2dbb51eee194eea","observation_id":"aa290208-8468-4ca7-ba1d-56d833df842a","resolution":{"observed_at":"2026-08-15T22:29:34.851961Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-08-15T22:27:53.662220Z","title":"Fisher, C","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.07299","last_updated":"2025-05-12T07:36:00Z","snapshot_observed_at":"2026-08-18T11:00:39.317864Z","submitted_at":"2025-05-12T07:36:00Z","title":"Interpretable Event Diagnosis in Water Distribution Networks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:53.662220Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/2505.07299"},"observation_digest":"sha256:73872aa936a9fc384492e2524a353f712d53fffa936773a1fc1c129815a669c3","observation_id":"c19c4e32-a87c-488e-87e3-d9f7bd5f3a3c","resolution":{"observed_at":"2026-08-15T22:27:53.662220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":"1801.01489","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"0c26fd5a-6dcb-4e57-bf77-3ac71d366e5d","year":2019},"citing_paper":{"arxiv_id":"2604.16536","last_updated":"2026-04-16T21:01:28Z","snapshot_observed_at":"2026-08-14T21:01:20.113538Z","submitted_at":"2026-04-16T21:01:28Z","title":"Towards Reliable Testing of Machine Unlearning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T11:24:29.533446Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/2604.16536"},"observation_digest":"sha256:0bc93174bf11e17dd0d9c59ece9e6c6bff92840d698e3f9f91ac7f16fc663557","observation_id":"22bda1e0-7008-4695-84ee-b8d1d9397072","resolution":{"observed_at":"2026-05-10T11:25:18.318896Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-08-11T00:36:59.145130Z","title":"arXiv preprint arXiv:1801.01489 , volume=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.07594","last_updated":"2026-08-06T04:29:58Z","snapshot_observed_at":"2026-08-18T17:41:06.909672Z","submitted_at":"2026-08-06T04:29:58Z","title":"Scaling Inherently Interpretable Language Models","version":1},"reference_index":188,"source":"arxiv_source","source_observed_at":"2026-08-11T00:36:59.145130Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/2608.07594"},"observation_digest":"sha256:c0a3f225714b9433a186760dea87369e914f10a19d1638bed148c5e1a683669d","observation_id":"3c68f210-8ba9-4c97-88e9-5913c5bbe371","resolution":{"observed_at":"2026-08-11T00:36:59.145130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01489","snapshot_observed_at":"2026-08-15T18:07:05.187858Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.13039","last_updated":"2026-08-13T10:05:25Z","snapshot_observed_at":"2026-08-16T23:11:30.778865Z","submitted_at":"2026-08-13T10:05:25Z","title":"On the global feature importance for interpretable and trustworthy heat demand forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T18:07:05.187858Z"},"links":{"cited_paper":"/paper/1801.01489","citing_paper":"/paper/2608.13039"},"observation_digest":"sha256:5f71e805a151964d069cd695064ac3b7e64564db2621b313823b5e29f3cd806f","observation_id":"51b610e5-50c2-4439-837e-ccbcaacc2870","resolution":{"observed_at":"2026-08-15T18:07:05.187858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1801.01489/citation-record","integrity":"/paper/1801.01489/integrity","json":"/paper/1801.01489/citation-record.json","paper":"/paper/1801.01489"},"outbound":[],"paper":{"arxiv_id":"1801.01489","last_updated":"2019-12-23T18:32:06Z","latest_version":5,"primary_category":"stat.ME","snapshot_observed_at":"2026-08-15T02:42:08.615392Z","submitted_at":"2018-01-04T18:53:04Z","title":"All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1801.01489."}