{"as_of":"2026-08-08T10:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d8051a04bb99b27742eec2ecfc82f9a0a4511ecb4daab25f933a4d41b1b897f3","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":"2026-08-04T22:39:13.590127Z","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":"2026-07-02T15:47:06.078210Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.00782","last_updated":"2025-02-02T12:40:22Z","snapshot_observed_at":"2026-07-06T20:29:45.369233Z","submitted_at":"2025-02-02T12:40:22Z","title":"Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00782","snapshot_observed_at":"2026-08-04T22:39:13.590127Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.07245","last_updated":"2025-09-08T21:43:41Z","snapshot_observed_at":"2026-08-07T23:04:02.619121Z","submitted_at":"2025-09-08T21:43:41Z","title":"IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T22:39:13.590127Z"},"links":{"cited_paper":"/paper/2502.00782","citing_paper":"/paper/2509.07245"},"observation_digest":"sha256:05475428ad10a189c3249ca725566747b11a7f1f1c7de3ad2eb3a3fdea1cf1e7","observation_id":"f84317d8-46b1-4da6-bc3f-3f175c7b2949","resolution":{"observed_at":"2026-08-04T22:39:13.590127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00782","last_updated":"2025-02-02T12:40:22Z","snapshot_observed_at":"2026-07-06T20:29:45.369233Z","submitted_at":"2025-02-02T12:40:22Z","title":"Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.00782","snapshot_observed_at":"2026-08-04T21:21:41.279652Z","title":"Transfer learning in physics-informed neu- ral networks: Full fine-tuning, lightweight fine-tuning, and low-rank adaptation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.08094","last_updated":"2025-09-09T19:05:09Z","snapshot_observed_at":"2026-08-06T11:27:44.440123Z","submitted_at":"2025-09-09T19:05:09Z","title":"Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion","version":1},"reference_index":108,"source":"pdf_text","source_observed_at":"2026-08-04T21:21:41.279652Z"},"links":{"cited_paper":"/paper/2502.00782","citing_paper":"/paper/2509.08094"},"observation_digest":"sha256:743de1ccfd89c2dee65526e7622efe66cf6e43d67216320b7423bf7ad729f559","observation_id":"7b2b276a-0ca8-4a12-9b95-c4a86c7cf889","resolution":{"observed_at":"2026-08-04T21:21:41.279652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.00782","last_updated":"2025-02-02T12:40:22Z","snapshot_observed_at":"2026-07-06T20:29:45.369233Z","submitted_at":"2025-02-02T12:40:22Z","title":"Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation","version":1},"cited_work":{"arxiv_id":"2502.00782","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.00782","snapshot_observed_at":"2026-07-02T15:47:06.078210Z","title":null,"venue":null,"work_id":"4d69425b-71c0-4357-9921-27814a879be9","year":2025},"citing_paper":{"arxiv_id":"2606.06171","last_updated":"2026-06-04T13:41:25Z","snapshot_observed_at":"2026-08-04T09:09:37.552112Z","submitted_at":"2026-06-04T13:41:25Z","title":"Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-27T23:34:49.530549Z"},"links":{"cited_paper":"/paper/2502.00782","citing_paper":"/paper/2606.06171"},"observation_digest":"sha256:f17f9d929ec3aaa423daea1d789b7098f39b8acad8ff6e143172988304605679","observation_id":"481dfa01-0518-48dc-ab64-812bf987895c","resolution":{"observed_at":"2026-07-02T15:47:06.079762Z","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/2502.00782/citation-record","integrity":"/paper/2502.00782/integrity","json":"/paper/2502.00782/citation-record.json","paper":"/paper/2502.00782"},"outbound":[],"paper":{"arxiv_id":"2502.00782","last_updated":"2025-02-02T12:40:22Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T20:29:45.369233Z","submitted_at":"2025-02-02T12:40:22Z","title":"Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation"},"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:2502.00782."}