{"as_of":"2026-08-08T15:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7f920db6eca8bc44b666e06749e94921a6e40ccb877e1a5c7dbae8256a48db67","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":"2026-08-07T18:37:52.046834Z","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":"2026-05-18T08:51:08.880764Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.16356","last_updated":"2020-06-29T20:22:16Z","snapshot_observed_at":"2026-07-31T01:26:19.887327Z","submitted_at":"2020-06-29T20:22:16Z","title":"High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.16356","snapshot_observed_at":"2026-08-07T18:37:52.046834Z","title":"High- fidelity machine learning approximations of large-scale optimal power flow","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.10330","last_updated":"2026-05-28T13:16:50Z","snapshot_observed_at":"2026-08-08T00:55:07.990526Z","submitted_at":"2025-02-14T17:43:08Z","title":"Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T18:37:52.046834Z"},"links":{"cited_paper":"/paper/2006.16356","citing_paper":"/paper/2502.10330"},"observation_digest":"sha256:1879fa87d3af92f296208565b9277c0a935d858c72fd7ff7f441c601b98fa712","observation_id":"2fe92e2b-49e4-4c57-8f3d-4a0f919dc109","resolution":{"observed_at":"2026-08-07T18:37:52.046834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.16356","last_updated":"2020-06-29T20:22:16Z","snapshot_observed_at":"2026-07-31T01:26:19.887327Z","submitted_at":"2020-06-29T20:22:16Z","title":"High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow","version":1},"cited_work":{"arxiv_id":"2006.16356","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.16356","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"High- Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow","venue":null,"work_id":"4812275b-a674-4cf2-9346-17f61d457d0a","year":2020},"citing_paper":{"arxiv_id":"2510.06860","last_updated":"2026-04-21T10:08:33Z","snapshot_observed_at":"2026-07-06T22:32:03.314860Z","submitted_at":"2025-10-08T10:28:46Z","title":"Towards Generalization of Graph Neural Networks for AC Optimal Power Flow","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-18T08:49:17.172538Z"},"links":{"cited_paper":"/paper/2006.16356","citing_paper":"/paper/2510.06860"},"observation_digest":"sha256:faaec0066c3bb6de36afc5bfa9374702d0274672e8e793b1445c5b4debb054eb","observation_id":"2bc19eab-3844-470d-b127-1da614fd9aea","resolution":{"observed_at":"2026-05-18T08:51:08.882876Z","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/2006.16356/citation-record","integrity":"/paper/2006.16356/integrity","json":"/paper/2006.16356/citation-record.json","paper":"/paper/2006.16356"},"outbound":[],"paper":{"arxiv_id":"2006.16356","last_updated":"2020-06-29T20:22:16Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-07-31T01:26:19.887327Z","submitted_at":"2020-06-29T20:22:16Z","title":"High-Fidelity Machine Learning Approximations of Large-Scale 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":"2026-08-08T06:32:00.761636+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:2006.16356."}