{"as_of":"2026-08-18T08:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b0fee48dd87550b9f145b63811a0af0210a8d08e15765e1d6a0bf9d0e2bf2841","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":"2026-08-18T06:34:40.430872+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":"2026-08-15T16:37:34.329147Z","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":"2026-06-30T14:44:45.449081Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.08481","last_updated":"2022-01-26T04:49:48Z","snapshot_observed_at":"2026-08-18T04:08:41.290657Z","submitted_at":"2021-11-12T19:01:23Z","title":"PySINDy: A comprehensive Python package for robust sparse system identification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08481","snapshot_observed_at":"2026-08-15T16:37:34.329147Z","title":"PySINDy: A comprehensive Python package for robust sparse system identifica- tion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.03036","last_updated":"2025-09-03T05:53:40Z","snapshot_observed_at":"2026-08-17T23:02:43.200175Z","submitted_at":"2025-09-03T05:53:40Z","title":"Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-15T16:37:34.329147Z"},"links":{"cited_paper":"/paper/2111.08481","citing_paper":"/paper/2509.03036"},"observation_digest":"sha256:ec87f7ba3619fc76e283330d801cd54e59c9e0e8fceda882fa605efc1394b242","observation_id":"20ed31af-612f-484c-9cc4-957d7e60c5a6","resolution":{"observed_at":"2026-08-15T16:37:34.329147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08481","last_updated":"2022-01-26T04:49:48Z","snapshot_observed_at":"2026-08-18T04:08:41.290657Z","submitted_at":"2021-11-12T19:01:23Z","title":"PySINDy: A comprehensive Python package for robust sparse system identification","version":2},"cited_work":{"arxiv_id":"2111.08481","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.08481","snapshot_observed_at":"2026-06-30T14:44:45.449081Z","title":"arXiv preprint arXiv:2111.08481 , year=","venue":null,"work_id":"0993d0b5-a22a-4e67-af7b-7a68f9c0affb","year":null},"citing_paper":{"arxiv_id":"2605.06756","last_updated":"2026-05-07T16:26:48Z","snapshot_observed_at":"2026-07-06T23:19:10.574595Z","submitted_at":"2026-05-07T16:26:48Z","title":"Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-11T01:56:28.921605Z"},"links":{"cited_paper":"/paper/2111.08481","citing_paper":"/paper/2605.06756"},"observation_digest":"sha256:97e6ba1185885dc1f93ee6dd304e7e39fc1cd5f4a13202ce0d8113e87a560eca","observation_id":"3681d175-d9bb-400c-9089-debb9c6b6827","resolution":{"observed_at":"2026-05-11T04:05:58.197533Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08481","last_updated":"2022-01-26T04:49:48Z","snapshot_observed_at":"2026-08-18T04:08:41.290657Z","submitted_at":"2021-11-12T19:01:23Z","title":"PySINDy: A comprehensive Python package for robust sparse system identification","version":2},"cited_work":{"arxiv_id":"2111.08481","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.08481","snapshot_observed_at":"2026-06-30T14:44:45.449081Z","title":"arXiv preprint arXiv:2111.08481 , year=","venue":null,"work_id":"0993d0b5-a22a-4e67-af7b-7a68f9c0affb","year":null},"citing_paper":{"arxiv_id":"2606.05202","last_updated":"2026-07-27T22:31:01Z","snapshot_observed_at":"2026-08-10T09:05:06.794412Z","submitted_at":"2026-05-22T17:14:48Z","title":"Multi-Fidelity Learning with Shallow Recurrent Decoders for Multi-Physics Applications","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-06-30T14:36:09.079063Z"},"links":{"cited_paper":"/paper/2111.08481","citing_paper":"/paper/2606.05202"},"observation_digest":"sha256:fb3757b47df2a259270f2de81c9fb92a6e0df6835ed71687826335cc156edfd1","observation_id":"80ed94a5-c6bc-4e9e-9477-df88341be515","resolution":{"observed_at":"2026-06-30T14:44:45.450851Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2111.08481/citation-record","integrity":"/paper/2111.08481/integrity","json":"/paper/2111.08481/citation-record.json","paper":"/paper/2111.08481"},"outbound":[],"paper":{"arxiv_id":"2111.08481","last_updated":"2022-01-26T04:49:48Z","latest_version":2,"primary_category":"eess.SY","snapshot_observed_at":"2026-08-18T04:08:41.290657Z","submitted_at":"2021-11-12T19:01:23Z","title":"PySINDy: A comprehensive Python package for robust sparse system identification"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2111.08481."}