{"as_of":"2026-08-11T13:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:701475d3984664cf5d9239e594a35d069946ce67ec88dc7b95e560a4fb20d494","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":"2026-08-11T06:34:44.6726+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":"2026-05-08T07:26:22.088809Z","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":"2026-05-12T00:21:23.443155Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2207.08483","last_updated":"2022-07-18T10:07:13Z","snapshot_observed_at":"2026-07-06T13:32:27.065726Z","submitted_at":"2022-07-18T10:07:13Z","title":"wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws","version":1},"cited_work":{"arxiv_id":"2207.08483","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08483","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"De Ryck, S","venue":null,"work_id":"9b9c0cb1-d566-4e81-96fc-92b7cff80d6d","year":2022},"citing_paper":{"arxiv_id":"2604.23350","last_updated":"2026-04-25T15:24:13Z","snapshot_observed_at":"2026-07-06T23:09:34.300163Z","submitted_at":"2026-04-25T15:24:13Z","title":"GeoFunFlow-3D: A Physics-Guided Generative Flow Matching Framework for High-Fidelity 3D Aerodynamic Inference over Complex Geometries","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-08T07:26:22.088809Z"},"links":{"cited_paper":"/paper/2207.08483","citing_paper":"/paper/2604.23350"},"observation_digest":"sha256:564d8d91cc9eb52b1adcea07a2be2f57867ff99bcd1800ab32e96a8997cc4d6e","observation_id":"5e17da4b-5a1c-4775-ba2e-c71c75d86228","resolution":{"observed_at":"2026-05-11T21:01:11.740361Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.08483","last_updated":"2022-07-18T10:07:13Z","snapshot_observed_at":"2026-07-06T13:32:27.065726Z","submitted_at":"2022-07-18T10:07:13Z","title":"wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws","version":1},"cited_work":{"arxiv_id":"2207.08483","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2207.08483","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"De Ryck, S","venue":null,"work_id":"9b9c0cb1-d566-4e81-96fc-92b7cff80d6d","year":2022},"citing_paper":{"arxiv_id":"2604.25985","last_updated":"2026-04-28T17:08:25Z","snapshot_observed_at":"2026-08-02T14:31:27.993830Z","submitted_at":"2026-04-28T17:08:25Z","title":"Learning Neural Operator Surrogates for the Black Hole Accretion Code","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-07T15:28:38.494462Z"},"links":{"cited_paper":"/paper/2207.08483","citing_paper":"/paper/2604.25985"},"observation_digest":"sha256:9fe0b7dfc92d310ca810e28949309f08ac07e9a6d1a0bb5b07062f26a1ffabeb","observation_id":"82978f0d-3415-44d9-b80c-54ba7b08ddf1","resolution":{"observed_at":"2026-05-12T00:21:23.516300Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2207.08483/citation-record","integrity":"/paper/2207.08483/integrity","json":"/paper/2207.08483/citation-record.json","paper":"/paper/2207.08483"},"outbound":[],"paper":{"arxiv_id":"2207.08483","last_updated":"2022-07-18T10:07:13Z","latest_version":1,"primary_category":"math.NA","snapshot_observed_at":"2026-07-06T13:32:27.065726Z","submitted_at":"2022-07-18T10:07:13Z","title":"wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2207.08483."}