{"as_of":"2026-08-10T04:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:84914ef00de259679f8c8603c084e0a42049d49a0050a6a4219391b072256f43","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":"2026-08-05T11:25:05.247153Z","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":"2026-06-28T19:32:35.090829Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2003.07339","last_updated":"2020-03-16T17:21:35Z","snapshot_observed_at":"2026-08-09T13:45:11.385315Z","submitted_at":"2020-03-16T17:21:35Z","title":"Reinforcement Learning for Electricity Network Operation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.07339","snapshot_observed_at":"2026-08-05T11:25:05.247153Z","title":"Reinforcement learning for electricity network operation","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2509.02861","last_updated":"2025-09-02T22:17:25Z","snapshot_observed_at":"2026-08-09T13:46:07.758100Z","submitted_at":"2025-09-02T22:17:25Z","title":"Power Grid Control with Graph-Based Distributed Reinforcement Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-05T11:25:05.247153Z"},"links":{"cited_paper":"/paper/2003.07339","citing_paper":"/paper/2509.02861"},"observation_digest":"sha256:d92e720a6625d8ec4fcad5211ed3efb2917e4bf6b766b598f68818a1c89f1c81","observation_id":"fda6eaed-fd7a-4101-9674-9136ca613be4","resolution":{"observed_at":"2026-08-05T11:25:05.247153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.07339","last_updated":"2020-03-16T17:21:35Z","snapshot_observed_at":"2026-08-09T13:45:11.385315Z","submitted_at":"2020-03-16T17:21:35Z","title":"Reinforcement Learning for Electricity Network Operation","version":1},"cited_work":{"arxiv_id":"2003.07339","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2003.07339","snapshot_observed_at":"2026-06-28T19:32:35.090829Z","title":"arXiv preprint arXiv:2003.07339 (2020)","venue":null,"work_id":"88ccb6c1-d3d8-4792-9658-54753df67012","year":2003},"citing_paper":{"arxiv_id":"2606.00561","last_updated":"2026-05-30T06:32:55Z","snapshot_observed_at":"2026-08-08T17:20:13.260793Z","submitted_at":"2026-05-30T06:32:55Z","title":"Interpretable Policy Distillation for Power Grid Topology Control","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T19:24:38.237381Z"},"links":{"cited_paper":"/paper/2003.07339","citing_paper":"/paper/2606.00561"},"observation_digest":"sha256:088c938c70a2ce124321be3129b37e7bccb843706e4fed35ce7222c7da1d1faf","observation_id":"02f90444-2c26-4e72-9e10-a7752cf1e993","resolution":{"observed_at":"2026-06-28T19:32:35.092274Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2003.07339/citation-record","integrity":"/paper/2003.07339/integrity","json":"/paper/2003.07339/citation-record.json","paper":"/paper/2003.07339"},"outbound":[],"paper":{"arxiv_id":"2003.07339","last_updated":"2020-03-16T17:21:35Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-09T13:45:11.385315Z","submitted_at":"2020-03-16T17:21:35Z","title":"Reinforcement Learning for Electricity Network Operation"},"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 2 inbound Pith citation observations for arXiv:2003.07339."}