{"as_of":"2026-08-08T05:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a061d9ff2081f91e4bc61fc28811c35a1f2fee6916ce8fd8cf846096431f48e6","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":"2026-08-07T06:34:17.273281+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":"2026-08-06T11:58:43.490134Z","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":"2026-07-02T11:26:54.526957Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.00214","last_updated":"2022-11-01T01:50:00Z","snapshot_observed_at":"2026-07-06T14:12:53.672775Z","submitted_at":"2022-11-01T01:50:00Z","title":"Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.00214","snapshot_observed_at":"2026-08-06T11:58:43.490134Z","title":"Pellegrin, B","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22370","last_updated":"2025-07-30T04:26:36Z","snapshot_observed_at":"2026-08-06T11:58:39.431523Z","submitted_at":"2025-07-30T04:26:36Z","title":"Prediction of acoustic field in 1-D uniform duct with varying mean flow and temperature using neural networks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T11:58:43.490134Z"},"links":{"cited_paper":"/paper/2211.00214","citing_paper":"/paper/2507.22370"},"observation_digest":"sha256:c90d94dc9cd3ee8bee89114532e83a38dce9b10ea09e0174b99d118715e85ba5","observation_id":"8e9b439c-393f-454d-8f64-b426a67e0c2b","resolution":{"observed_at":"2026-08-06T11:58:43.490134Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.00214","last_updated":"2022-11-01T01:50:00Z","snapshot_observed_at":"2026-07-06T14:12:53.672775Z","submitted_at":"2022-11-01T01:50:00Z","title":"Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows","version":1},"cited_work":{"arxiv_id":"2211.00214","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.00214","snapshot_observed_at":"2026-07-02T11:26:54.526957Z","title":"Transfer learn- ing with physics-informed neural networks for efficient simulation of branched flows.arXiv preprint arXiv:2211.00214, 2022","venue":null,"work_id":"6a036f4e-c68b-4bc3-b37d-43b67b0fa187","year":2022},"citing_paper":{"arxiv_id":"2606.04447","last_updated":"2026-06-03T04:48:05Z","snapshot_observed_at":"2026-07-06T23:44:33.095008Z","submitted_at":"2026-06-03T04:48:05Z","title":"ATLAS-NN: Adaptive Transfer Learnable Symplectic-aware Neural Network for Long-Time Hamiltonian Dynamics","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-28T03:50:38.792407Z"},"links":{"cited_paper":"/paper/2211.00214","citing_paper":"/paper/2606.04447"},"observation_digest":"sha256:6698bf9289442be43eb5e927a3a6ccb4a9564defe79e0db58b283d694faa274d","observation_id":"b5dcaa19-12e7-4aae-bcdf-5f06be5f52af","resolution":{"observed_at":"2026-07-02T11:26:54.528766Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2211.00214/citation-record","integrity":"/paper/2211.00214/integrity","json":"/paper/2211.00214/citation-record.json","paper":"/paper/2211.00214"},"outbound":[],"paper":{"arxiv_id":"2211.00214","last_updated":"2022-11-01T01:50:00Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T14:12:53.672775Z","submitted_at":"2022-11-01T01:50:00Z","title":"Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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:2211.00214."}