{"as_of":"2026-08-14T19:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8c6722b22d21567ff359b8c1f25cb54aff700fc17b02f7b87756d52fa95c3a3a","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:49:00.828053Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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":"2026-08-06T21:29:50.097805Z","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":"2026-08-06T20:21:15.205534Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.00738","snapshot_observed_at":"2026-08-06T21:29:50.097805Z","title":"Learning Weather Models from Data with WSINDy,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.24101","last_updated":"2025-06-30T17:51:12Z","snapshot_observed_at":"2026-08-08T06:58:44.309351Z","submitted_at":"2025-06-30T17:51:12Z","title":"Learning Structured Population Models from Data with WSINDy","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:50.097805Z"},"links":{"cited_paper":"/paper/2501.00738","citing_paper":"/paper/2506.24101"},"observation_digest":"sha256:07ed10ffde76c36a028bcab1cb4e760e118a363264bb2c96e87ef1e3b528e54d","observation_id":"a23db9d9-ba50-473d-9561-0ee08da2d22e","resolution":{"observed_at":"2026-08-06T21:29:50.097805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"cited_work":{"arxiv_id":"2501.00738","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.00738","snapshot_observed_at":"2026-08-06T20:21:15.205534Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","venue":"physics.geo-ph","work_id":"b2fba95f-f811-4652-baa3-5d283a358837","year":2025},"citing_paper":{"arxiv_id":"2507.03206","last_updated":"2025-07-03T22:36:34Z","snapshot_observed_at":"2026-08-14T10:23:51.592577Z","submitted_at":"2025-07-03T22:36:34Z","title":"Weak Form Scientific Machine Learning: Test Function Construction for System Identification","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T20:21:11.360393Z"},"links":{"cited_paper":"/paper/2501.00738","citing_paper":"/paper/2507.03206"},"observation_digest":"sha256:fb4c65fe3c4412f02f8897947d0e719a0d6370320e04a98aa225af0478afb45b","observation_id":"c1c6e894-07d7-4905-9a18-b5c4348d46e4","resolution":{"observed_at":"2026-08-06T20:21:15.256442Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.00738/citation-record","integrity":"/paper/2501.00738/integrity","json":"/paper/2501.00738/citation-record.json","paper":"/paper/2501.00738"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.666012Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.666012Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:4a59bc63d0cd30321a12acddca687c32811c1efd61d0e70833898b820d033f22","observation_id":"bfc36f35-1d57-4a36-bfeb-2455ae5f748d","resolution":{"observed_at":"2026-08-10T22:49:00.666012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.672927Z","title":", Rochanotes , Ross, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.672927Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:e5328dbd396015e3d1938db8cb646be3881427eae642fb6184e1f3eac8252255","observation_id":"42d85b09-b866-4ca0-9f1a-bf167f96a197","resolution":{"observed_at":"2026-08-10T22:49:00.672927Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.21957/wnmguimihe","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:01.133035Z","title":"\\ Th \\'e paut, J N","venue":null,"work_id":"5912c679-9042-4136-a995-53d2704f4258","year":2008},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.678332Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:852d4cb115659c88bc69edbc00fe261193a4bc34edf60a8baf12c90c6e6f8b33","observation_id":"ef9b12dd-ad9d-4223-9583-c9b03b1f621b","resolution":{"observed_at":"2026-08-10T22:49:01.138488Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.684254Z","title":", Messenger, D A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.684254Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:c5e3633d4c4ab981f784e9c6e31fab86c250181a8899f7794729e1cb8375476c","observation_id":"cf403d6a-e480-40d1-b3e9-f86160da06e8","resolution":{"observed_at":"2026-08-10T22:49:00.684254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:01.881209Z","title":", Messenger, D A","venue":null,"work_id":"ce7d7aac-350f-4ec7-991d-901d63ca4d63","year":2024},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.690010Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:056a47955bedbf7218ea444a2f2102dbb101430bb6fa5ff72d7b659a0c088fbc","observation_id":"e1da2df9-5969-4018-9c82-bddb86bb2c75","resolution":{"observed_at":"2026-08-10T22:49:01.886656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.695212Z","title":", Proctor, J L","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.695212Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:13baf47467a6130e42043f3f4d289939dd5442c014ca42a9592e3d4043b69a40","observation_id":"b71a96a5-6edb-4c92-b84c-a2781e40e54b","resolution":{"observed_at":"2026-08-10T22:49:00.695212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.700495Z","title":", Vasil, G M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.700495Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:8a4dfa91075f156b1754f3d5e126af753ce6accff5aa6def7cb368f167e3d65d","observation_id":"a802f174-1bbc-4f47-9f6f-ac895cda1744","resolution":{"observed_at":"2026-08-10T22:49:00.700495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"3490.1950","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:01.827693Z","title":", Fj \\\"o rtoft, R","venue":null,"work_id":"2f4de5b8-801c-4708-871f-e6d87c53c83e","year":1950},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.706005Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:b89943e695f2bc906b7641db8e803e8d876173d1538f24a543c98566313bd11f","observation_id":"006405ad-b1ce-4dec-9ada-356d33b09dd1","resolution":{"observed_at":"2026-08-10T22:49:01.836375Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.710372Z","title":"APACrefauthors \\ 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.710372Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:e084159acf592d43161e2b381db340f2b0cf52d7c2ee0ff3d3d7c9646c215105","observation_id":"43353db4-0aa0-4888-bc79-c9d7fbb632bd","resolution":{"observed_at":"2026-08-10T22:49:00.710372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:01.864201Z","title":"APACrefauthors \\ 2021 10","venue":null,"work_id":"e3ba3b6e-2762-4895-823e-336e86ed8cb4","year":2021},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.714684Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:de0013a26a9a95686a615989d77a60b38b1e7d504d458371d4d0c81b6bd72f32","observation_id":"f8a474d1-29f0-445d-ac6e-3ecc1b022822","resolution":{"observed_at":"2026-08-10T22:49:01.869682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3402/tellusa.v56i5.14436","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:01.056997Z","title":", Scott, R K","venue":null,"work_id":"718a3400-04ed-4180-abd9-37fcc35ff48a","year":2004},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.719125Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:d556cd0d82ecf633696990a7ec03cd854b54e94c677de65b498cc0c462af9f86","observation_id":"2e1baca6-4931-4032-a238-07f8da451d48","resolution":{"observed_at":"2026-08-10T22:49:01.063502Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.724053Z","title":", Golden, M R","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.724053Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:fab0a1f3ff94dd3db002c4495ca3ef3d5d62227c8e8097da82aaa8840e48516d","observation_id":"39130a3b-0873-48ba-baf4-052f9a577927","resolution":{"observed_at":"2026-08-10T22:49:00.724053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2407.14129","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:01.739293Z","title":", Maddix, D C","venue":null,"work_id":"34895592-7f65-42f5-8bb9-d777bb74ea84","year":2024},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.728632Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:ca3d4f23d02cb4d53033bad29772cd342ba8a70ab6f0b2cc2a23a1ab7d1e689e","observation_id":"2e58010c-b991-40a6-acb2-f75c5fa0661c","resolution":{"observed_at":"2026-08-10T22:49:01.747021Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.732992Z","title":", Yuval, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.732992Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:ca808e3a87dc86f88d671bc1f983176bf351be8a470490909ea5303c81396d34","observation_id":"e985ff09-5d7b-4067-ad39-a8829e991306","resolution":{"observed_at":"2026-08-10T22:49:00.732992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1029/2022gl100009","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:01.016744Z","title":", Smith, L M","venue":null,"work_id":"ed080108-5891-4d56-be7d-51989b3709d1","year":2022},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.737769Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:d4f33ca81fa274d81c7901eca0899637fcf168d7a501ff6a394961f367d0ded6","observation_id":"c162223d-fe37-46e6-87c5-26369af019c3","resolution":{"observed_at":"2026-08-10T22:49:01.022486Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.0800","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:01.655142Z","title":", Nardini, J T","venue":null,"work_id":"fa04f278-1df0-45c4-a2b2-4bdfcbc263f0","year":2020},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.742353Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:9c7f252e31e93dc6a1eae50307e8a341f80caacf86866bb93cfbecad0bd82495","observation_id":"645bc922-8c11-49a4-b94e-a6f1e8cf0073","resolution":{"observed_at":"2026-08-10T22:49:01.663434Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.746604Z","title":", Sanchez-Gonzalez , A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.746604Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:f9761871e391c7a832979ab19b6b413c430e514ce89943804373b79a27e1d45d","observation_id":"297f9b14-4600-48ba-8341-2501a73f2d43","resolution":{"observed_at":"2026-08-10T22:49:00.746604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.751033Z","title":"\\ Herrmann, B","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.751033Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:6a933de2ebbd875d925553ce82b52db6822433f83e74a1c33de7203949fd42ec","observation_id":"36a92d93-82ef-450a-a9fe-1bfc73ac7e12","resolution":{"observed_at":"2026-08-10T22:49:00.751033Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.756252Z","title":", Perlman, E","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.756252Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:bc5d1efaadf843a2a7c3a1204ad1bf57f4cd6f9766c7771ba600aa02560d9630","observation_id":"1c27b107-c6b6-49d7-acf6-3aafbe9c5245","resolution":{"observed_at":"2026-08-10T22:49:00.756252Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.760981Z","title":"\\ Cahen, G M","venue":null,"work_id":null,"year":1965},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.760981Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:848a73ce3cb586a370ea18509b9a62db7ef2c19e95562c7e99b2e0231ccc8b7a","observation_id":"7adf5c5e-db60-4d3a-8e56-272ed65ec919","resolution":{"observed_at":"2026-08-10T22:49:00.760981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.765810Z","title":"APACrefauthors \\ 1963 03","venue":null,"work_id":null,"year":1963},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.765810Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:da57c7a743212c0819917add00b5fe010cf8ec20ccdad96ea9e5dc1a34d753b2","observation_id":"9e3f6595-6148-41bd-a91f-d10f8f605195","resolution":{"observed_at":"2026-08-10T22:49:00.765810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1017/s0022112084001750","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.952666Z","title":"APACrefauthors \\ 1984 09","venue":null,"work_id":"09e1d8d8-533a-4f48-8fdb-fc4527307d58","year":1984},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.770427Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:028cf29b5b526203434bb37a34ce123113ed09f3e606f5f296d038f1e4865fc6","observation_id":"04684832-1c59-4456-8756-3813c96c8911","resolution":{"observed_at":"2026-08-10T22:49:00.958668Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/atmos14071107","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.933649Z","title":", Meneveau, C","venue":null,"work_id":"cbd4d843-2035-4e17-8900-757eff27b175","year":2023},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.774892Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:64eed5fe7f79f2493a2329f0b72ee5b8461ea73a947f5f79ec1d4dec07529925","observation_id":"68d7906e-3842-4e19-9684-f33de43d1456","resolution":{"observed_at":"2026-08-10T22:49:00.940884Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.779368Z","title":"\\ Bortz, D M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.779368Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:56d28835ad7c4648b5ed7d9f808ef5ea6bef434bfa7407d0c8df573f8cabbecc","observation_id":"9660c3f9-28fc-46e9-9bf4-a896d081f0c7","resolution":{"observed_at":"2026-08-10T22:49:00.779368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.784038Z","title":"\\ Bortz, D M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.784038Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:d5489f18ed88adacadbdab0e18d6885fd474d38c4d839e079f778fb438279572","observation_id":"743f23df-e4fe-4756-9f88-c24a15dabb07","resolution":{"observed_at":"2026-08-10T22:49:00.784038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.788513Z","title":", Dwyer, G","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.788513Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:fe7087b48a4117b195a663f72f4b0804d1aa05dd80b17610e0e8cc0ee9475cfb","observation_id":"c4fdea2f-d3e3-407c-b6d6-d31f5e1bc512","resolution":{"observed_at":"2026-08-10T22:49:00.788513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.06751","last_updated":"2024-09-10T13:59:17Z","snapshot_observed_at":"2026-08-12T22:47:43.615000Z","submitted_at":"2024-09-10T13:59:17Z","title":"The Weak Form Is Stronger Than You Think","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.06751","snapshot_observed_at":"2026-08-10T22:49:00.792920Z","title":", Tran, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.792920Z"},"links":{"cited_paper":"/paper/2409.06751","citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:5d954dd40a4e5688c5330995ec14d27660919ec8e02816509e9824d81c4f6b81","observation_id":"7be1936d-6c06-4f34-8a74-a51a1556216a","resolution":{"observed_at":"2026-08-10T22:49:00.792920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:01.847394Z","title":", Tran, A","venue":null,"work_id":"50e2a389-faf2-4fdc-82c2-041e69ed7ce5","year":2024},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.798395Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:d8f8cce16017ddd77038d7978d4886084f37be5672394b1211411d12b535c5ab","observation_id":"d1c898a0-9bb3-42c7-97ad-3a5ab1a74c3b","resolution":{"observed_at":"2026-08-10T22:49:01.852441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.803278Z","title":", Hoyer, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.803278Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:92877843057f77a4dc5f8bf96d0585ffbaa777766e6ca6eea1032d7ed9fd5678","observation_id":"52327923-9d2b-4c80-9ff7-dfcf8a2bb398","resolution":{"observed_at":"2026-08-10T22:49:00.803278Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.808283Z","title":", Kageorge, L M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.808283Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:49361b8fe5507dd17438b887e212171d25bc659ec1c145d5f29cde39bd84b74b","observation_id":"9047dff7-bb98-4c93-8e80-ec71aaaa055c","resolution":{"observed_at":"2026-08-10T22:49:00.808283Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.813862Z","title":", Brunton, S L","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.813862Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:fe5b8ccffce4c4e2e80dc13a33c3e6ceeda589c0980c8dde6afed5dfa3e207e2","observation_id":"b75ee837-ca5f-46d3-8288-a4d1e63bad81","resolution":{"observed_at":"2026-08-10T22:49:00.813862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.818556Z","title":", Messenger, D A","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.818556Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:08463fd600547c39ef19a5bef07f1fec8a866b3aed9b0901e1c64942b88c162d","observation_id":"9a6bc6df-0642-45a2-8738-eaa01d9fd4bc","resolution":{"observed_at":"2026-08-10T22:49:00.818556Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/b0-12-227090-8/00139-1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.875302Z","title":"APACrefauthors \\ 2003","venue":null,"work_id":"4609ebc5-97c4-4637-ba72-51214433b07d","year":2003},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.823166Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:6030f006658e6bf893bd9c8bbde827245828c7c667088ff1788c64bf4b78224a","observation_id":"f661bbce-8706-4273-9c4b-bd44aa34c232","resolution":{"observed_at":"2026-08-10T22:49:00.881697Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T22:49:00.828053Z","title":"\\ Bolton, T","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T22:49:00.828053Z"},"links":{"citing_paper":"/paper/2501.00738"},"observation_digest":"sha256:2af26af79c7f0a9d853b1034df7ec67198b9d69131cd01f8292a0467edd7425c","observation_id":"dfec9e70-0e2e-4323-b7a6-17d204a8a99d","resolution":{"observed_at":"2026-08-10T22:49:00.828053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.00738","last_updated":"2025-07-05T07:34:49Z","latest_version":2,"primary_category":"physics.geo-ph","snapshot_observed_at":"2026-08-10T22:41:34.633442Z","submitted_at":"2025-01-01T06:03:07Z","title":"Learning Physically Interpretable Atmospheric Models from Data with WSINDy"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":8,"verified_fuzzy":3},"total_outbound_references":34},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2501.00738."}