{"as_of":"2026-08-05T21:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f458a8eb10309e288844159dbafa39c27362738241ad7b01587f7d13d5ad3f45","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T18:23:26.016442Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-05-24T17:46:17.322396Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1901.04966","last_updated":"2019-01-15T18:40:06Z","snapshot_observed_at":"2026-07-06T07:26:54.072746Z","submitted_at":"2019-01-15T18:40:06Z","title":"Identifying and Correcting Label Bias in Machine Learning","version":1},"cited_work":{"arxiv_id":"1901.04966","doi":null,"metadata_source":"pith","pith_arxiv_id":"1901.04966","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Identifying and Correcting Label Bias in Machine Learning","venue":"cs.LG","work_id":"281d6d26-a0e2-4c81-b534-dc117e6c0756","year":2019},"citing_paper":{"arxiv_id":"1907.09754","last_updated":"2019-07-23T08:35:23Z","snapshot_observed_at":"2026-07-06T08:09:31.884004Z","submitted_at":"2019-07-23T08:35:23Z","title":"Controlling biases and diversity in diverse image-to-image translation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-24T17:44:56.810167Z"},"links":{"cited_paper":"/paper/1901.04966","citing_paper":"/paper/1907.09754"},"observation_digest":"sha256:e76cc0181a562331f07b6f22f6fa2652021b66f7785629922872649627853028","observation_id":"6ae99095-c462-4675-9dba-55bc31b4c5d6","resolution":{"observed_at":"2026-05-24T17:46:17.325292Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.04966","last_updated":"2019-01-15T18:40:06Z","snapshot_observed_at":"2026-07-06T07:26:54.072746Z","submitted_at":"2019-01-15T18:40:06Z","title":"Identifying and Correcting Label Bias in Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.04966","snapshot_observed_at":"2026-08-05T18:23:26.016442Z","title":"URL: http://arxiv.org/abs/1901.04966","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2508.14741","last_updated":"2025-08-20T14:40:21Z","snapshot_observed_at":"2026-08-05T18:22:56.440384Z","submitted_at":"2025-08-20T14:40:21Z","title":"CaTE Data Curation for Trustworthy AI","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-05T18:23:26.016442Z"},"links":{"cited_paper":"/paper/1901.04966","citing_paper":"/paper/2508.14741"},"observation_digest":"sha256:d3fa950ae65ab1322859a7bbfc7953af923b89b0c116a6fd6e3bc270b6255974","observation_id":"1b06387e-c3dc-4e6c-9f8d-9d4fca35f5c2","resolution":{"observed_at":"2026-08-05T18:23:26.016442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1901.04966/citation-record","integrity":"/paper/1901.04966/integrity","json":"/paper/1901.04966/citation-record.json","paper":"/paper/1901.04966"},"outbound":[],"paper":{"arxiv_id":"1901.04966","last_updated":"2019-01-15T18:40:06Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T07:26:54.072746Z","submitted_at":"2019-01-15T18:40:06Z","title":"Identifying and Correcting Label Bias in Machine Learning"},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1901.04966."}