{"as_of":"2026-08-05T14:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7752e4e9d1979553b776040b76703187b608d7d4904a18a20b20ea31a7e6a162","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":"2026-08-05T06:32:48.257954+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":"2026-05-08T01:35:01.859305Z","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":"2026-05-11T23:06:21.162838Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.22453","last_updated":"2025-06-14T21:38:36Z","snapshot_observed_at":"2026-07-06T21:48:49.620457Z","submitted_at":"2025-06-14T21:38:36Z","title":"Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks","version":1},"cited_work":{"arxiv_id":"2506.22453","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.22453","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Data-Driven Surrog ate Modeling of DSMC Solutions Using Deep Neural Networks","venue":null,"work_id":"8ceb7b88-35e6-4e84-9608-25ed6fa8b0ba","year":2025},"citing_paper":{"arxiv_id":"2604.24225","last_updated":"2026-04-27T09:31:45Z","snapshot_observed_at":"2026-07-06T23:10:20.676482Z","submitted_at":"2026-04-27T09:31:45Z","title":"Multilevel radial basis function surrogates for noise-robust DSMC-CFD coupling","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-08T01:35:01.859305Z"},"links":{"cited_paper":"/paper/2506.22453","citing_paper":"/paper/2604.24225"},"observation_digest":"sha256:5a2d5f4b8df8aae74639bc29dab587190f05ee06ae5d9c33bf4d4f81595d90ae","observation_id":"94ec8a15-c2c8-4b26-9358-0c15f47d40a3","resolution":{"observed_at":"2026-05-11T23:06:21.166109Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.22453/citation-record","integrity":"/paper/2506.22453/integrity","json":"/paper/2506.22453/citation-record.json","paper":"/paper/2506.22453"},"outbound":[],"paper":{"arxiv_id":"2506.22453","last_updated":"2025-06-14T21:38:36Z","latest_version":1,"primary_category":"physics.comp-ph","snapshot_observed_at":"2026-07-06T21:48:49.620457Z","submitted_at":"2025-06-14T21:38:36Z","title":"Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks"},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2506.22453."}