{"as_of":"2026-08-08T14:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:45a0bb4d14eee951a631e71d1f4418528682b2da965d0f3cffd2b8ec48850bcc","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T11:09:19.642567Z","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":"2026-07-03T19:38:52.991476Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2105.01160","last_updated":"2021-05-14T09:29:11Z","snapshot_observed_at":"2026-08-04T12:53:02.350336Z","submitted_at":"2021-05-03T20:31:20Z","title":"The Tracking Machine Learning challenge : Throughput phase","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.01160","snapshot_observed_at":"2026-08-07T11:09:19.642567Z","title":"Amrouche et al., The Tracking Machine Learning Challenge: Throughput Phase, Comput","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.03415","last_updated":"2025-06-03T21:48:29Z","snapshot_observed_at":"2026-08-07T11:01:25.266008Z","submitted_at":"2025-06-03T21:48:29Z","title":"Physics and Computing Performance of the EggNet Tracking Pipeline","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T11:09:19.642567Z"},"links":{"cited_paper":"/paper/2105.01160","citing_paper":"/paper/2506.03415"},"observation_digest":"sha256:8b65dbc8d9c57f3340ad21aaeba59cc40b013fb257c6331bf4404a6d97221a1d","observation_id":"1b59fb74-7a38-45c6-a80c-6e4e0a1df501","resolution":{"observed_at":"2026-08-07T11:09:19.642567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.01160","last_updated":"2021-05-14T09:29:11Z","snapshot_observed_at":"2026-08-04T12:53:02.350336Z","submitted_at":"2021-05-03T20:31:20Z","title":"The Tracking Machine Learning challenge : Throughput phase","version":2},"cited_work":{"arxiv_id":"2105.01160","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2105.01160","snapshot_observed_at":"2026-07-03T19:38:52.991476Z","title":"2105.01160 , archiveprefix =","venue":null,"work_id":"f1fd8674-a367-425b-90f3-894dd2ee0342","year":null},"citing_paper":{"arxiv_id":"2607.01354","last_updated":"2026-07-01T18:11:57Z","snapshot_observed_at":"2026-08-02T03:43:43.094925Z","submitted_at":"2026-07-01T18:11:57Z","title":"Local Conformal Predictions for Calibrated Surrogates","version":1},"reference_index":127,"source":"arxiv_source","source_observed_at":"2026-07-03T19:29:34.070294Z"},"links":{"cited_paper":"/paper/2105.01160","citing_paper":"/paper/2607.01354"},"observation_digest":"sha256:a41a399f66ef1f2072d0262e0070be504b1e33e47dbd24a36f1efc37a46ad1c5","observation_id":"3db7f3a4-8f03-455e-aa3f-ba81dbfbe3d7","resolution":{"observed_at":"2026-07-03T19:38:52.993086Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2105.01160/citation-record","integrity":"/paper/2105.01160/integrity","json":"/paper/2105.01160/citation-record.json","paper":"/paper/2105.01160"},"outbound":[],"paper":{"arxiv_id":"2105.01160","last_updated":"2021-05-14T09:29:11Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T12:53:02.350336Z","submitted_at":"2021-05-03T20:31:20Z","title":"The Tracking Machine Learning challenge : Throughput phase"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2105.01160."}