{"as_of":"2026-08-09T10:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:11bbe66996b50386d971268428270703794dcce14e7f4dce1969ebf529ba1a30","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":"2026-08-09T06:31:02.800959+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":"2026-08-08T14:40:50.103273Z","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":"2026-08-08T14:40:50.377842Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1910.13427","last_updated":"2019-10-29T17:44:35Z","snapshot_observed_at":"2026-08-06T07:10:21.551292Z","submitted_at":"2019-10-29T17:44:35Z","title":"Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications","version":1},"cited_work":{"arxiv_id":"1910.13427","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.13427","snapshot_observed_at":"2026-08-08T14:40:50.377842Z","title":"Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications","venue":"cs.LG","work_id":"31c9f3e7-ba33-45ff-a0a8-69c362e7da54","year":2019},"citing_paper":{"arxiv_id":"2502.06695","last_updated":"2025-02-10T17:18:54Z","snapshot_observed_at":"2026-08-08T14:35:05.271504Z","submitted_at":"2025-02-10T17:18:54Z","title":"FairDropout: Using Example-Tied Dropout to Enhance Generalization of Minority Groups","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-08T14:40:50.103273Z"},"links":{"cited_paper":"/paper/1910.13427","citing_paper":"/paper/2502.06695"},"observation_digest":"sha256:52d0a11f879b25eeeaaf7335a7f6128a9cca55c004e6c43ee5f826b330462017","observation_id":"5a7c8717-2cf8-4690-b485-71f668df23a1","resolution":{"observed_at":"2026-08-08T14:40:50.384145Z","resolver_source":"local_arxiv","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":"1910.13427","last_updated":"2019-10-29T17:44:35Z","snapshot_observed_at":"2026-08-06T07:10:21.551292Z","submitted_at":"2019-10-29T17:44:35Z","title":"Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13427","snapshot_observed_at":"2026-08-02T21:27:38.210726Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2602.20114","last_updated":"2026-07-23T08:26:34Z","snapshot_observed_at":"2026-08-06T14:08:11.376070Z","submitted_at":"2026-02-23T18:33:16Z","title":"Benchmarking Unlearning for Vision Transformers","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-02T21:27:38.210726Z"},"links":{"cited_paper":"/paper/1910.13427","citing_paper":"/paper/2602.20114"},"observation_digest":"sha256:dd26580ea613f4e83a7c5bcca62d9718981a1ad3d1a8c85b7ae92f7b1709445b","observation_id":"fc076f7f-2ea3-4736-8466-01ef2336da90","resolution":{"observed_at":"2026-08-02T21:27:38.210726Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1910.13427/citation-record","integrity":"/paper/1910.13427/integrity","json":"/paper/1910.13427/citation-record.json","paper":"/paper/1910.13427"},"outbound":[],"paper":{"arxiv_id":"1910.13427","last_updated":"2019-10-29T17:44:35Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T07:10:21.551292Z","submitted_at":"2019-10-29T17:44:35Z","title":"Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications"},"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 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.13427."}