{"as_of":"2026-08-08T08:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cd73d789c4f4be42e36d3d742bb26f17ac9e8e3041c5e0baf8f91e1a54269e1b","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":"2026-08-07T13:40:19.794757Z","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":"2026-08-07T13:40:21.289137Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.13815","last_updated":"2024-11-21T03:40:34Z","snapshot_observed_at":"2026-07-06T19:53:30.020280Z","submitted_at":"2024-11-21T03:40:34Z","title":"FLRNet: A Deep Learning Method for Regressive Reconstruction of Flow Field From Limited Sensor Measurements","version":1},"cited_work":{"arxiv_id":"2411.13815","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.13815","snapshot_observed_at":"2026-08-07T13:40:21.289137Z","title":"FLRNet: A Deep Learning Method for Regressive Reconstruction of Flow Field From Limited Sensor Measurements","venue":"physics.flu-dyn","work_id":"1f6daa34-82db-46d1-ae9f-38207f9e2d60","year":2024},"citing_paper":{"arxiv_id":"2505.21421","last_updated":"2025-05-27T16:49:58Z","snapshot_observed_at":"2026-08-07T13:26:11.919849Z","submitted_at":"2025-05-27T16:49:58Z","title":"A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T13:40:19.794757Z"},"links":{"cited_paper":"/paper/2411.13815","citing_paper":"/paper/2505.21421"},"observation_digest":"sha256:3847ad6ab01fd1688bb596691087382b761690c424a4a0b596248a4996624653","observation_id":"d59c9d8f-04e9-4f91-b3a8-58213e9ca627","resolution":{"observed_at":"2026-08-07T13:40:21.360320Z","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/2411.13815/citation-record","integrity":"/paper/2411.13815/integrity","json":"/paper/2411.13815/citation-record.json","paper":"/paper/2411.13815"},"outbound":[],"paper":{"arxiv_id":"2411.13815","last_updated":"2024-11-21T03:40:34Z","latest_version":1,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-07-06T19:53:30.020280Z","submitted_at":"2024-11-21T03:40:34Z","title":"FLRNet: A Deep Learning Method for Regressive Reconstruction of Flow Field From Limited Sensor Measurements"},"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:2411.13815."}