{"as_of":"2026-08-11T09:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dd8bb8dce563b3a3dff2d7fec1a7598d82ff1c2e5d97338ea8b5caf9eff32a5f","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":"2026-08-11T06:34:44.6726+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":"2026-08-09T19:36:57.886203Z","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":"2026-07-02T01:36:26.318469Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1910.08842","last_updated":"2019-10-19T20:47:13Z","snapshot_observed_at":"2026-07-06T08:30:47.148345Z","submitted_at":"2019-10-19T20:47:13Z","title":"Machine Learning for AC Optimal Power Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.08842","snapshot_observed_at":"2026-08-09T19:36:57.886203Z","title":"Machine learning for ac optimal power flow","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2502.00304","last_updated":"2025-02-01T03:59:15Z","snapshot_observed_at":"2026-08-10T13:57:19.985281Z","submitted_at":"2025-02-01T03:59:15Z","title":"HoP: Homeomorphic Polar Learning for Hard Constrained Optimization","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-09T19:36:57.886203Z"},"links":{"cited_paper":"/paper/1910.08842","citing_paper":"/paper/2502.00304"},"observation_digest":"sha256:c4d2feef41fa2e72b0e8db823e2e846a091e212ee48c7db7f6c22b0fbf142d4f","observation_id":"b837bb0c-8008-477b-af53-017dc11481f2","resolution":{"observed_at":"2026-08-09T19:36:57.886203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.08842","last_updated":"2019-10-19T20:47:13Z","snapshot_observed_at":"2026-07-06T08:30:47.148345Z","submitted_at":"2019-10-19T20:47:13Z","title":"Machine Learning for AC Optimal Power Flow","version":1},"cited_work":{"arxiv_id":"1910.08842","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1910.08842","snapshot_observed_at":"2026-07-02T01:36:26.318469Z","title":"Machine learning for ac optimal power flow,","venue":null,"work_id":"50f25d23-9c7b-41df-9f39-a662f79f901b","year":1910},"citing_paper":{"arxiv_id":"2606.03125","last_updated":"2026-06-02T04:10:18Z","snapshot_observed_at":"2026-08-07T23:32:08.399976Z","submitted_at":"2026-06-02T04:10:18Z","title":"Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T11:36:55.493267Z"},"links":{"cited_paper":"/paper/1910.08842","citing_paper":"/paper/2606.03125"},"observation_digest":"sha256:43708e30805f782996c6817e0541bdd0e38b03f1c592228d875c8c1efceacc8e","observation_id":"60c178c7-4705-4f71-bdc7-a098cd6e1681","resolution":{"observed_at":"2026-07-02T01:36:26.321348Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1910.08842/citation-record","integrity":"/paper/1910.08842/integrity","json":"/paper/1910.08842/citation-record.json","paper":"/paper/1910.08842"},"outbound":[],"paper":{"arxiv_id":"1910.08842","last_updated":"2019-10-19T20:47:13Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T08:30:47.148345Z","submitted_at":"2019-10-19T20:47:13Z","title":"Machine Learning for AC Optimal Power Flow"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.08842."}