{"as_of":"2026-08-19T23:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3e606d4f30a3e04842f958bae1faefc9cf87df2d40886d945235281a08eab3b8","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T20:40:01.017003Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2502.00088/citation-record","integrity":"/paper/2502.00088/integrity","json":"/paper/2502.00088/citation-record.json","paper":"/paper/2502.00088"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T20:40:01.236580Z","title":"Notions of explainability and evaluation approaches for explainable artifi- cial intelligence","venue":null,"work_id":"8316d880-482f-4360-93af-18a72ec3263e","year":2021},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:00.963829Z"},"links":{"citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:438ed3e7525311a2c0bdb9b2e6e4e1da07d2294eb6be0656e10f655181606352","observation_id":"23a5d89f-c486-4a0f-8c04-d5d77705d367","resolution":{"observed_at":"2026-08-09T20:40:01.241604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-09T20:40:01.220318Z","title":"A review of evaluation approaches for explainable AI with applications in cardiol- ogy","venue":null,"work_id":"d9181ef7-592d-4fb5-8220-24aaf51a5dda","year":2024},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:00.969548Z"},"links":{"citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:6128f0a3e79fda33925bad6ab1bac9114155cb2a4292bf5c70ef0a8637e82aa0","observation_id":"25ca49ed-e172-4428-8931-338d833f166f","resolution":{"observed_at":"2026-08-09T20:40:01.225730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-09T20:40:01.204052Z","title":"A benchmark for interpretability methods in deep neural networks","venue":null,"work_id":"a8ac58f8-f7f3-4764-b412-f842ebe021fc","year":2019},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:00.974220Z"},"links":{"citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:718b9057db296952cf33fa31d9617baaf9c1a7c565928f2a370b3ddd867b81b9","observation_id":"171f6e4c-2c82-450d-adbd-b465e306155f","resolution":{"observed_at":"2026-08-09T20:40:01.209378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-09T20:40:01.187913Z","title":"Multilayer Perceptron of Software Complexity Metrics for Explainable Multi- collinearity Mitigation and Defect Localization","venue":null,"work_id":"134cfd94-b189-4efd-bdc0-4a10baa730c8","year":2025},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:00.978722Z"},"links":{"citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:224394586adc8cacf45ab99773322951988fca5f06ae7024c611e5923624bdbc","observation_id":"e2f810a0-bf05-439f-a9fc-1c9e0a5bb6f7","resolution":{"observed_at":"2026-08-09T20:40:01.193165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.07874","last_updated":"2017-11-25T03:53:32Z","snapshot_observed_at":"2026-08-14T20:59:00.743160Z","submitted_at":"2017-05-22T17:38:10Z","title":"A Unified Approach to Interpreting Model Predictions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.07874","snapshot_observed_at":"2026-08-09T20:40:00.983678Z","title":"A unified approach to interpreting model predictions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:00.983678Z"},"links":{"cited_paper":"/paper/1705.07874","citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:6c17a8462ae0786d02b701cc53a774401811b9b5b8668af8aaec3e5d4a29a4d3","observation_id":"e76f9f62-5abb-411b-a102-63ccc59bea20","resolution":{"observed_at":"2026-08-09T20:40:00.983678Z","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":"2026-08-09T20:40:01.171201Z","title":"Characterizing the Contribution of Dependent Features in XAI Methods","venue":null,"work_id":"8f208c49-731e-40c7-bfc6-26c7f726dec0","year":2024},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:00.988899Z"},"links":{"citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:1b2b3875e298d5f08ab05a6730e358d37f2f12411c6f6eb3f233f8e74d2b70be","observation_id":"99b86c28-95b2-496b-b43e-1d9c7fe33614","resolution":{"observed_at":"2026-08-09T20:40:01.176631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-09T20:40:01.154371Z","title":"Permutation importance: a corrected feature importance measure","venue":null,"work_id":"3de139d3-9e5e-4af3-9b2c-3354a83f7c78","year":2010},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:00.994194Z"},"links":{"citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:3c491769f64d3246b050ca7f36529bf1fc874b1fab926b6afb0345f94c8caf1f","observation_id":"9076880c-30e4-400b-af7e-edc06ad44d11","resolution":{"observed_at":"2026-08-09T20:40:01.159618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-09T20:40:01.137427Z","title":"CDC National Health Report: leading causes of morbidity and mortality and associated behavioral risk and protective factors–United States, 2005-2013","venue":null,"work_id":"2eb382cd-91b6-4075-8493-65a37d936441","year":2014},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:00.998507Z"},"links":{"citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:01055bca014ce00ee7a156568303290a5d89ece9427bb852cf7c191f29efc41c","observation_id":"3bc04053-532b-440b-89f5-26a72795ed70","resolution":{"observed_at":"2026-08-09T20:40:01.143123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-09T20:40:01.118219Z","title":"Modeling wine preferences by data mining from physicochemical properties","venue":null,"work_id":"85528b03-115e-406c-99ba-245b63f9c5de","year":2009},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:01.003322Z"},"links":{"citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:e4ad14c1cd6636aa3581e0927d84a557489644fc069228873f6b25b2d63b5339","observation_id":"02e7ece6-adf2-4144-a88f-4aa7d5cb3bc0","resolution":{"observed_at":"2026-08-09T20:40:01.123471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.11293","last_updated":"2020-03-09T21:20:06Z","snapshot_observed_at":"2026-08-10T12:38:34.811873Z","submitted_at":"2019-11-26T00:32:23Z","title":"Efficient Saliency Maps for Explainable AI","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.11293","snapshot_observed_at":"2026-08-09T20:40:01.007693Z","title":"Efficient saliency maps for explainable AI","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:01.007693Z"},"links":{"cited_paper":"/paper/1911.11293","citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:aa630d4f9d32fa688a080fdbd49b7019417c2748d23ce5508ae1ba9f9acc55c7","observation_id":"26453df1-a54e-44f2-8f6d-4d56114c72c7","resolution":{"observed_at":"2026-08-09T20:40:01.007693Z","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":"2026-08-09T20:40:01.101990Z","title":"Explainable artificial intelligence approaches: Challenges and perspectives","venue":null,"work_id":"b61c7e01-4a10-4437-8574-ed790cfee274","year":2021},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:01.012530Z"},"links":{"citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:f1801931a3ef7e926cf72ab66c60a427b03ee904cb42f2dfe32df25bed869c3f","observation_id":"48828727-9ba1-48f7-a7a0-4d3616c3ecaf","resolution":{"observed_at":"2026-08-09T20:40:01.107310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-09T20:40:01.083021Z","title":"Cardiovascular risk factors and physical activity for the prevention of cardiovas- cular diseases in the elderly","venue":null,"work_id":"0ad0a057-c54d-4e06-a9d5-def7bfd5529d","year":2021},"citing_paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T20:40:01.017003Z"},"links":{"citing_paper":"/paper/2502.00088"},"observation_digest":"sha256:7ea4c138f39c8eaa8addf7342930fcd6939ae01151a1a48b96be0ad8787d53ed","observation_id":"6ec06455-6933-4c1f-8b76-4ec39513523a","resolution":{"observed_at":"2026-08-09T20:40:01.090722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.00088","last_updated":"2025-01-31T17:18:43Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T07:18:14.817898Z","submitted_at":"2025-01-31T17:18:43Z","title":"Re-Visiting Explainable AI Evaluation Metrics to Identify The Most Informative Features"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":10},"total_outbound_references":12},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2502.00088."}