{"as_of":"2026-08-16T16:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:857855698f669d7c9dc8c53b683d721fa1b24a2f09c93c9ea5f50e748b3824dc","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T11:09:33.628017Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.09718/citation-record","integrity":"/paper/1908.09718/integrity","json":"/paper/1908.09718/citation-record.json","paper":"/paper/1908.09718"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1901.04592","last_updated":"2019-01-14T22:35:26Z","snapshot_observed_at":"2026-08-15T18:24:49.918908Z","submitted_at":"2019-01-14T22:35:26Z","title":"Interpretable machine learning: definitions, methods, and applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.04592","snapshot_observed_at":"2026-08-14T11:09:33.511390Z","title":"J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.511390Z"},"links":{"cited_paper":"/paper/1901.04592","citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:268c18e88f77be3799338c5233b45eddbcd4f8e316e7f64a108d8e9f3a3cf747","observation_id":"316af8f1-491f-41e4-a72b-df87d8de8c18","resolution":{"observed_at":"2026-08-14T11:09:33.511390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1702.08608","last_updated":"2017-03-02T19:32:10Z","snapshot_observed_at":"2026-08-13T03:10:58.738032Z","submitted_at":"2017-02-28T02:19:20Z","title":"Towards A Rigorous Science of Interpretable Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1702.08608","snapshot_observed_at":"2026-08-14T11:09:33.517386Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.517386Z"},"links":{"cited_paper":"/paper/1702.08608","citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:81a4beddb5f62f9b7685d2f7c47fca4703a2059d02b9054e61e709d6f98f0b77","observation_id":"196340fc-79c3-46d4-b679-df027d46fd1a","resolution":{"observed_at":"2026-08-14T11:09:33.517386Z","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":"2026-08-14T11:09:34.043327Z","title":"M., & Lee, S","venue":null,"work_id":"49809ebc-d79a-4fee-ba9d-7c7318617a3a","year":2017},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.524156Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:adac55d193c509817edbe51394fcec3b2813d05520ebc6b97655f72e8f8e5868","observation_id":"4e5d2e3f-9b11-47e4-a1df-e733406f1431","resolution":{"observed_at":"2026-08-14T11:09:34.048195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:34.025121Z","title":null,"venue":null,"work_id":"cd989c33-7484-4ce5-9469-a597df3d59e5","year":2018},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.530518Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:b006017c5ed421ea577f7d3f4690c36405e070f28d92f9d4b4dffd4304f575fb","observation_id":"ba5e2221-8599-4662-a0f4-557c9c03912a","resolution":{"observed_at":"2026-08-14T11:09:34.031438Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:34.007678Z","title":null,"venue":null,"work_id":"b3c0dd24-85bd-4dc5-954a-9f4f99f6bc33","year":2018},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.537084Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:d75e40f69795f3f90934a8022a9e677bf4ca0645741191a279c18a80277b7f0d","observation_id":"6d34f807-f7e6-44a9-aec2-7c27f3e6706a","resolution":{"observed_at":"2026-08-14T11:09:34.013008Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.988813Z","title":"Interpretable machine learning. A Guide for Making Black Box Models Explainable","venue":null,"work_id":"66b5ed88-9ac8-47ae-add3-c6e446faa052","year":2019},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.542211Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:e31cdf6184b000ff7d6d277c892e213d36cc041ac0890c62b3f7d39684b47804","observation_id":"91331656-1fd5-4540-837b-044ac9e58ecb","resolution":{"observed_at":"2026-08-14T11:09:33.993818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.00561","last_updated":"2019-04-01T04:42:07Z","snapshot_observed_at":"2026-08-14T16:53:55.957820Z","submitted_at":"2019-04-01T04:42:07Z","title":"VINE: Visualizing Statistical Interactions in Black Box Models","version":1},"cited_work":{"arxiv_id":"1904.00561","doi":null,"metadata_source":"pith","pith_arxiv_id":"1904.00561","snapshot_observed_at":"2026-08-14T11:09:33.717784Z","title":"VINE: Visualizing Statistical Interactions in Black Box Models","venue":"cs.LG","work_id":"fe0a3737-5166-4404-a6cf-0b19ecae81ff","year":2019},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.547780Z"},"links":{"cited_paper":"/paper/1904.00561","citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:ec3e98a213d7e55be5cdb7af9d92142f7db32cb5b8ef360e3316a1163353ec4d","observation_id":"edc856bd-b73a-4ce7-ab4b-27485cd8a4de","resolution":{"observed_at":"2026-08-14T11:09:33.725756Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.971339Z","title":null,"venue":null,"work_id":"afb92383-3f8f-45bd-afc8-4f4d4c99f912","year":2015},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.553152Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:a43ad38ba75264f569c2e15c321432a86fcea96b6055a18dd524b78ef1b94cf7","observation_id":"3824c1d4-14fe-4023-8871-c4d0f2c18f73","resolution":{"observed_at":"2026-08-14T11:09:33.977246Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.954324Z","title":null,"venue":null,"work_id":"3d7dd20a-ef83-4812-9e09-056375838947","year":2017},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.559348Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:9d051867976fda05b3996530ca6fe62992001933b2e78b52847c45238fca3100","observation_id":"98c9f329-8594-4855-aec3-a090046ca7ad","resolution":{"observed_at":"2026-08-14T11:09:33.960426Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.937614Z","title":null,"venue":null,"work_id":"bab4be14-6e92-4af9-af0b-51eadd62259a","year":1921},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.564447Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:37ed684d7549c9345b1364ae4b48864bcd141ea1c4079e24e450caf70d8abe9f","observation_id":"d94a58f3-475a-4826-932c-e7c3c8b370d2","resolution":{"observed_at":"2026-08-14T11:09:33.943151Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.918017Z","title":null,"venue":null,"work_id":"b39f30ed-c317-4506-b857-828fa4398538","year":1985},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.569350Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:2aba3f7e7218599699bc34ec2ab545a83b4c1d055228a2febeee47e4bba77b0f","observation_id":"36961e41-8b14-4db0-a60e-240a2ad62462","resolution":{"observed_at":"2026-08-14T11:09:33.925165Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.573947Z","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":"2026-08-16T11:43:34.049288Z","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:ff9fe38f94389dc45611978ca1bd49153d46b2b7b17619e97a5a4c5a07bb8b33","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:09:33.899409Z","title":null,"venue":null,"work_id":"06d5bcd1-3de0-4ea5-aa3e-2a13e6c64703","year":2018},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.580859Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:b172d9ebc8354c0c62b68b1b1c13057caf4f89b50f7df2773eeef7e4d62cbd19","observation_id":"15ae4125-5dfa-46a3-a339-3a9b5d9fa565","resolution":{"observed_at":"2026-08-14T11:09:33.904991Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.881054Z","title":null,"venue":null,"work_id":"e5f3a1fa-d9fd-4728-a41d-3e83ce31064b","year":1980},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.585471Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:44d6ababe8c88bbdca61641726d4b8dfb9fbb6c7a26c0b0254b51d6d4633009a","observation_id":"de60475c-eb56-44bc-a8c5-411f666a6869","resolution":{"observed_at":"2026-08-14T11:09:33.886030Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.860862Z","title":"D., (2017) Decomposing the R-squared of a Regression Using the Shapley Value in SAS®","venue":null,"work_id":"8269b3dd-c9ab-4c32-932d-a60ff6f1bf36","year":2017},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.589764Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:f606d8c07ea4b15d2ef553256b2901fa77fd229dc3ff3f6383ab3737aee692cf","observation_id":"64830f47-608c-4fd4-bb03-6b05e7a86699","resolution":{"observed_at":"2026-08-14T11:09:33.867852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.844052Z","title":null,"venue":null,"work_id":"05545bc5-2e08-451c-9698-6fea484c246c","year":1953},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.595260Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:562f0b074c992f58b2b1a9907e921f89f0cb16cc19501edc7277992eafd55d9d","observation_id":"1bb88412-14bd-423f-ac60-c51e7ce8dcb9","resolution":{"observed_at":"2026-08-14T11:09:33.848357Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.03888","last_updated":"2019-03-07T00:06:09Z","snapshot_observed_at":"2026-08-14T19:46:58.523390Z","submitted_at":"2018-02-12T04:23:03Z","title":"Consistent Individualized Feature Attribution for Tree Ensembles","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.03888","snapshot_observed_at":"2026-08-14T11:09:33.599476Z","title":"M., Erion, G","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.599476Z"},"links":{"cited_paper":"/paper/1802.03888","citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:5b28c802e60185e27c1fe76ffb2e1a073295f9b390ee5b07ba18a35155b233a9","observation_id":"d6770acd-6a56-4413-8cf7-15f2004e019a","resolution":{"observed_at":"2026-08-14T11:09:33.599476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.10464","last_updated":"2020-02-06T13:31:18Z","snapshot_observed_at":"2026-08-14T16:57:06.872888Z","submitted_at":"2019-03-25T16:57:11Z","title":"Explaining individual predictions when features are dependent: More accurate approximations to Shapley values","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.10464","snapshot_observed_at":"2026-08-14T11:09:33.604424Z","title":"Explaining individual predictions when features are dependent: More accurate approximations to Shapley values","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.604424Z"},"links":{"cited_paper":"/paper/1903.10464","citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:3b7f96583d2743cd3b8f972287ace8a1f4a2bfc1e689ba7831ee08ca1e8e6fff","observation_id":"17aba2b5-ec7c-40f6-8950-25c177cb0cf4","resolution":{"observed_at":"2026-08-14T11:09:33.604424Z","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":"2026-08-14T11:09:33.826607Z","title":"Cortez, A","venue":null,"work_id":"e09e5971-3889-4983-9bda-c2b20014ca90","year":2009},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.610811Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:0e7b4d2128a8727cfe0ee7b146e5ddbed0c7990eb573fc6ee6d545affe8d901a","observation_id":"5436592c-7e78-4fe5-8a5c-82a28b79a34a","resolution":{"observed_at":"2026-08-14T11:09:33.832641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.807892Z","title":"Cortez & A","venue":null,"work_id":"bd5b52b0-69c8-4f6a-8f90-19f4c6f7b2de","year":2007},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.616477Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:c139fe6fbf9ba268957a932d823cd81bfc762a3b1c8d213e1f281f9d4087b728","observation_id":"0e818d51-4bfd-4639-a424-8dd8c413afc4","resolution":{"observed_at":"2026-08-14T11:09:33.814164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.791530Z","title":"& Graff, C","venue":null,"work_id":"b585c9a6-f3c0-4260-adba-508fcab0f0dd","year":2019},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.622249Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:89637954ac32ac9b3f4c013dc47a567eff427271694975908b317b41c0907390","observation_id":"9fa2a562-ad50-4e3e-9dde-d0d7c6260b6b","resolution":{"observed_at":"2026-08-14T11:09:33.797147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T11:09:33.774516Z","title":"V., & Gulin, A","venue":null,"work_id":"050d227a-169a-4028-99a3-ebdef1fe790e","year":2018},"citing_paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T11:09:33.628017Z"},"links":{"citing_paper":"/paper/1908.09718"},"observation_digest":"sha256:b71cfcb80792057ab20105adbf8135b3d87c0f9a1f4510fccaf1a4cfc765d744","observation_id":"d017abda-a987-4ca7-9f25-cd47a7dabb7c","resolution":{"observed_at":"2026-08-14T11:09:33.780056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.09718","last_updated":"2019-08-26T14:56:36Z","latest_version":1,"primary_category":"stat.ME","snapshot_observed_at":"2026-08-16T11:43:34.049288Z","submitted_at":"2019-08-26T14:56:36Z","title":"Shapley Decomposition of R-Squared in Machine Learning Models"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":1,"verified_fuzzy":7},"total_outbound_references":22},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:1908.09718."}