{"as_of":"2026-08-14T21:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7efce023912536a8b742843e8fa1967113161d36e691db511c7057ace7ba68d7","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":"2026-08-14T06:32:32.682623+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":"2026-08-12T21:08:21.039244Z","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":"2026-07-04T09:49:44.672684Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2010.04591","last_updated":"2020-10-09T14:18:31Z","snapshot_observed_at":"2026-08-13T21:22:58.997686Z","submitted_at":"2020-10-09T14:18:31Z","title":"Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04591","snapshot_observed_at":"2026-08-12T21:08:21.039244Z","title":"Physics-informed gaussian process regres- sion for probabilistic states estimation and forecasting in power grids,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.09123","last_updated":"2024-11-14T01:44:39Z","snapshot_observed_at":"2026-08-12T20:57:48.056466Z","submitted_at":"2024-11-14T01:44:39Z","title":"Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T21:08:21.039244Z"},"links":{"cited_paper":"/paper/2010.04591","citing_paper":"/paper/2411.09123"},"observation_digest":"sha256:e1b87383db654d92263a203b193e115adb7e396f385a3fa4629722f39e740181","observation_id":"f3f4d56e-780a-426a-88ad-7a4390483a10","resolution":{"observed_at":"2026-08-12T21:08:21.039244Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04591","last_updated":"2020-10-09T14:18:31Z","snapshot_observed_at":"2026-08-13T21:22:58.997686Z","submitted_at":"2020-10-09T14:18:31Z","title":"Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids","version":1},"cited_work":{"arxiv_id":"2010.04591","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.04591","snapshot_observed_at":"2026-07-04T09:49:44.672684Z","title":"arXiv preprint arXiv:2010.04591 , year=","venue":null,"work_id":"75f33431-29a5-4b40-8703-d53707eaa4cb","year":2010},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"cited_paper":"/paper/2010.04591","citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:1bded4bba28dc84167231fc69dec812734cf4a1efb203b91c330ba84cdde431f","observation_id":"17c26206-8d47-419a-8581-7e8a030fcfb1","resolution":{"observed_at":"2026-07-04T09:49:44.673911Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2010.04591/citation-record","integrity":"/paper/2010.04591/integrity","json":"/paper/2010.04591/citation-record.json","paper":"/paper/2010.04591"},"outbound":[],"paper":{"arxiv_id":"2010.04591","last_updated":"2020-10-09T14:18:31Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-13T21:22:58.997686Z","submitted_at":"2020-10-09T14:18:31Z","title":"Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2010.04591."}