{"as_of":"2026-08-08T15:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d7700eec6cfe6af752a0c4a280eafcc73dba062ca84d43890de0b9a4846bf880","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:13:19.083703Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":20,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2010.00072","last_updated":"2020-10-03T19:22:53Z","snapshot_observed_at":"2026-08-07T04:15:12.744801Z","submitted_at":"2020-09-30T19:29:21Z","title":"Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.00072","snapshot_observed_at":"2026-08-07T15:13:19.083703Z","title":", author Mustafa , M","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.16041","last_updated":"2025-05-21T21:47:43Z","snapshot_observed_at":"2026-08-07T15:05:45.452276Z","submitted_at":"2025-05-21T21:47:43Z","title":"Physics-based machine learning for mantle convection simulations","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T15:13:19.083703Z"},"links":{"cited_paper":"/paper/2010.00072","citing_paper":"/paper/2505.16041"},"observation_digest":"sha256:e80fcf1b0c146b9b5f6cc92a1bca88e28bffdf68e0f4f46446f107980c9dc96a","observation_id":"c749b474-74e4-4127-b684-4ea04bb8020e","resolution":{"observed_at":"2026-08-07T15:13:19.083703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.00072","last_updated":"2020-10-03T19:22:53Z","snapshot_observed_at":"2026-08-07T04:15:12.744801Z","submitted_at":"2020-09-30T19:29:21Z","title":"Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations","version":2},"cited_work":{"arxiv_id":"2010.00072","doi":"10.48550/arxiv.2010.00072","metadata_source":"arxiv_reference","pith_arxiv_id":"2010.00072","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"2010.00072 , archiveprefix =","venue":"arXiv (Cornell University)","work_id":"35bf2f18-34f3-43ee-b550-a272ca138865","year":2010},"citing_paper":{"arxiv_id":"2606.08854","last_updated":"2026-06-07T21:47:31Z","snapshot_observed_at":"2026-08-05T12:25:41.110491Z","submitted_at":"2026-06-07T21:47:31Z","title":"sGPO: Trading Inference FLOPs for Training Efficiency in RLVR","version":1},"reference_index":138,"source":"arxiv_source","source_observed_at":"2026-06-27T18:23:58.023982Z"},"links":{"cited_paper":"/paper/2010.00072","citing_paper":"/paper/2606.08854"},"observation_digest":"sha256:1c2df690b756a688f622a95bc4b5db40de177c60e35895bcf77d2b6b8b67d826","observation_id":"beface70-b7e4-4aec-a7fa-7c9539df167c","resolution":{"observed_at":"2026-07-02T23:17:29.268043Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2010.00072","last_updated":"2020-10-03T19:22:53Z","snapshot_observed_at":"2026-08-07T04:15:12.744801Z","submitted_at":"2020-09-30T19:29:21Z","title":"Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations","version":2},"cited_work":{"arxiv_id":"2010.00072","doi":"10.48550/arxiv.2010.00072","metadata_source":"arxiv_reference","pith_arxiv_id":"2010.00072","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"2010.00072 , archiveprefix =","venue":"arXiv (Cornell University)","work_id":"35bf2f18-34f3-43ee-b550-a272ca138865","year":2010},"citing_paper":{"arxiv_id":"2606.10335","last_updated":"2026-06-09T02:29:14Z","snapshot_observed_at":"2026-08-06T06:04:56.649157Z","submitted_at":"2026-06-09T02:29:14Z","title":"A Physics-Informed B-Spline Framework for Continuous Approximation of Flow Data","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-27T11:23:58.613887Z"},"links":{"cited_paper":"/paper/2010.00072","citing_paper":"/paper/2606.10335"},"observation_digest":"sha256:39beaad445e0c2b9825ac9215d87cfaf38ec3f8c0bc3d74ecb3eba6cf8463b8c","observation_id":"eb27fc12-0482-40ec-9be3-36c58502d4d4","resolution":{"observed_at":"2026-06-27T11:30:53.236038Z","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/2010.00072/citation-record","integrity":"/paper/2010.00072/integrity","json":"/paper/2010.00072/citation-record.json","paper":"/paper/2010.00072"},"outbound":[],"paper":{"arxiv_id":"2010.00072","last_updated":"2020-10-03T19:22:53Z","latest_version":2,"primary_category":"physics.comp-ph","snapshot_observed_at":"2026-08-07T04:15:12.744801Z","submitted_at":"2020-09-30T19:29:21Z","title":"Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations"},"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 3 inbound Pith citation observations for arXiv:2010.00072."}