{"as_of":"2026-08-08T16:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ec5b8d5522824025b44531f8a5c9567f2d729442230e110d3e9e550c1ba43196","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T21:36:33.264933Z","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":"2026-08-07T21:36:34.145440Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2203.00636","last_updated":"2022-03-09T21:16:42Z","snapshot_observed_at":"2026-07-06T12:42:59.343294Z","submitted_at":"2022-03-01T17:25:40Z","title":"Distributional Reinforcement Learning for Scheduling of Chemical Production Processes","version":2},"cited_work":{"arxiv_id":"2203.00636","doi":null,"metadata_source":"pith","pith_arxiv_id":"2203.00636","snapshot_observed_at":"2026-08-07T21:36:34.145440Z","title":"Distributional Reinforcement Learning for Scheduling of Chemical Production Processes","venue":"eess.SY","work_id":"5f4659ae-612d-464a-893d-4f635ef44d03","year":2022},"citing_paper":{"arxiv_id":"2502.09417","last_updated":"2025-02-13T15:40:39Z","snapshot_observed_at":"2026-08-07T21:32:46.428389Z","submitted_at":"2025-02-13T15:40:39Z","title":"A Survey of Reinforcement Learning for Optimization in Automation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T21:36:33.264933Z"},"links":{"cited_paper":"/paper/2203.00636","citing_paper":"/paper/2502.09417"},"observation_digest":"sha256:4643d0f35ead4987c64ff2fda66217d16c0e0ad55b1e7f73e4b2f5535e5e577c","observation_id":"e85d2636-7391-4951-88ac-b4fe01ec0ec8","resolution":{"observed_at":"2026-08-07T21:36:34.150530Z","resolver_source":"local_arxiv","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/2203.00636/citation-record","integrity":"/paper/2203.00636/integrity","json":"/paper/2203.00636/citation-record.json","paper":"/paper/2203.00636"},"outbound":[],"paper":{"arxiv_id":"2203.00636","last_updated":"2022-03-09T21:16:42Z","latest_version":2,"primary_category":"eess.SY","snapshot_observed_at":"2026-07-06T12:42:59.343294Z","submitted_at":"2022-03-01T17:25:40Z","title":"Distributional Reinforcement Learning for Scheduling of Chemical Production Processes"},"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 1 inbound Pith citation observation for arXiv:2203.00636."}