{"as_of":"2026-08-10T04:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8c823a15b153ff8c10b5d909fec0c68eda162393f56345de863df0a57cb3b7a5","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:43:53.786241Z","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":"2026-05-18T15:02:41.209886Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1911.00068","last_updated":"2022-08-22T00:58:03Z","snapshot_observed_at":"2026-07-06T08:33:56.199723Z","submitted_at":"2019-10-31T19:26:33Z","title":"Confident Learning: Estimating Uncertainty in Dataset Labels","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.00068","snapshot_observed_at":"2026-08-06T17:43:53.786241Z","title":"Northcutt, Lu Jiang, and Isaac L","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.10088","last_updated":"2025-07-14T09:15:22Z","snapshot_observed_at":"2026-08-08T23:10:24.065143Z","submitted_at":"2025-07-14T09:15:22Z","title":"Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T17:43:53.786241Z"},"links":{"cited_paper":"/paper/1911.00068","citing_paper":"/paper/2507.10088"},"observation_digest":"sha256:2e006474d4f733b9b6535d8e654bc727307ca1e6f82e9b1e31b5c185d8bb71c0","observation_id":"e008506c-986c-45fe-95a1-b9a1fe5fa804","resolution":{"observed_at":"2026-08-06T17:43:53.786241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.00068","last_updated":"2022-08-22T00:58:03Z","snapshot_observed_at":"2026-07-06T08:33:56.199723Z","submitted_at":"2019-10-31T19:26:33Z","title":"Confident Learning: Estimating Uncertainty in Dataset Labels","version":6},"cited_work":{"arxiv_id":"1911.00068","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.00068","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite","venue":null,"work_id":"a5f7512b-347a-4015-85d9-acb36a9da911","year":1911},"citing_paper":{"arxiv_id":"2509.20786","last_updated":"2026-05-13T15:28:04Z","snapshot_observed_at":"2026-07-06T22:30:41.141182Z","submitted_at":"2025-09-25T06:13:25Z","title":"LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-18T15:01:49.645065Z"},"links":{"cited_paper":"/paper/1911.00068","citing_paper":"/paper/2509.20786"},"observation_digest":"sha256:79172359c80ed1aab09d5e7710a5f68cc3655292877000816c802cff58f3358c","observation_id":"4f722416-ef1b-42ef-8ccc-a375e6ad1f13","resolution":{"observed_at":"2026-05-18T15:02:41.212989Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.00068","last_updated":"2022-08-22T00:58:03Z","snapshot_observed_at":"2026-07-06T08:33:56.199723Z","submitted_at":"2019-10-31T19:26:33Z","title":"Confident Learning: Estimating Uncertainty in Dataset Labels","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.00068","snapshot_observed_at":"2026-08-03T11:01:24.784050Z","title":"Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2601.07965","last_updated":"2026-06-28T07:55:15Z","snapshot_observed_at":"2026-08-09T08:33:13.501013Z","submitted_at":"2026-01-12T19:59:03Z","title":"When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-03T11:01:24.784050Z"},"links":{"cited_paper":"/paper/1911.00068","citing_paper":"/paper/2601.07965"},"observation_digest":"sha256:ba8dc9a9c9d56f3f9aa85c518b1a018e3e7a8622493a321841d18786ba9d6511","observation_id":"5eab5441-4bd9-451b-a161-3eee451cecd3","resolution":{"observed_at":"2026-08-03T11:01:24.784050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.00068","last_updated":"2022-08-22T00:58:03Z","snapshot_observed_at":"2026-07-06T08:33:56.199723Z","submitted_at":"2019-10-31T19:26:33Z","title":"Confident Learning: Estimating Uncertainty in Dataset Labels","version":6},"cited_work":{"arxiv_id":"1911.00068","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1911.00068","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite","venue":null,"work_id":"a5f7512b-347a-4015-85d9-acb36a9da911","year":1911},"citing_paper":{"arxiv_id":"2604.26147","last_updated":"2026-04-28T22:12:15Z","snapshot_observed_at":"2026-07-06T23:11:52.954360Z","submitted_at":"2026-04-28T22:12:15Z","title":"A Data-Centric Framework for Intraoperative Fluorescence Lifetime Imaging for Glioma Surgical Guidance","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-07T16:44:45.403876Z"},"links":{"cited_paper":"/paper/1911.00068","citing_paper":"/paper/2604.26147"},"observation_digest":"sha256:216386bdb55db00dc7dc9ff1cc4f2533b80e9c7e9257e3c7a06b6c4904206c1a","observation_id":"9854f8c1-c899-44d6-a512-912f090ab722","resolution":{"observed_at":"2026-05-11T23:36:14.073575Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.00068","last_updated":"2022-08-22T00:58:03Z","snapshot_observed_at":"2026-07-06T08:33:56.199723Z","submitted_at":"2019-10-31T19:26:33Z","title":"Confident Learning: Estimating Uncertainty in Dataset Labels","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.00068","snapshot_observed_at":"2026-08-02T10:02:37.068883Z","title":"and Jiang, Lu and Chuang, Isaac L","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2607.16239","last_updated":"2026-06-26T00:31:00Z","snapshot_observed_at":"2026-08-09T20:03:40.575632Z","submitted_at":"2026-06-26T00:31:00Z","title":"BACON: Budgeted Human Calibration for Modeling and Evaluation with Multiple AI Judges","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-02T10:02:37.068883Z"},"links":{"cited_paper":"/paper/1911.00068","citing_paper":"/paper/2607.16239"},"observation_digest":"sha256:a63b3417e3746576571c4cfe5fe092abd51fcb39164bfb980d0c89ccb1ad26fe","observation_id":"eb514b99-9dfb-41ae-ac39-b5140524b364","resolution":{"observed_at":"2026-08-02T10:02:37.068883Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1911.00068/citation-record","integrity":"/paper/1911.00068/integrity","json":"/paper/1911.00068/citation-record.json","paper":"/paper/1911.00068"},"outbound":[],"paper":{"arxiv_id":"1911.00068","last_updated":"2022-08-22T00:58:03Z","latest_version":6,"primary_category":"stat.ML","snapshot_observed_at":"2026-07-06T08:33:56.199723Z","submitted_at":"2019-10-31T19:26:33Z","title":"Confident Learning: Estimating Uncertainty in Dataset Labels"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1911.00068."}