{"as_of":"2026-08-07T14:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dc1a01546cbe5f743d430b770e91a224dadee308d41c59a0aef4c243e1f61a09","coverage":[{"denominator":68,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":68,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-23T03:15:12.654574Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.12396/citation-record","integrity":"/paper/2502.12396/integrity","json":"/paper/2502.12396/citation-record.json","paper":"/paper/2502.12396"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5194/bg-20-2671-2023","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Should we apply bias correction to global and regional climate model data?","venue":"Biogeosciences","work_id":"b07cc283-b587-4dcf-90e9-08f08f137414","year":2023},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:1bac56904e7a827a83c7dd58083506778836557d29b2b8d2dbcf77a6e35d8f0d","observation_id":"dfaaa725-e67b-4e89-9760-332363bc0269","resolution":{"observed_at":"2026-05-23T03:15:21.021149Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Borchers, B","venue":null,"work_id":"97931b0c-a376-4d9a-ab42-982af83df5a9","year":2013},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:f1f216ca6ffb1e53b7f4f39ae4ce993b1233ca744ca0f4275e7c68dac75fffff","observation_id":"0ae88fa0-0fce-4a43-8fc2-d18c67c3eb98","resolution":{"observed_at":"2026-05-23T03:15:21.423744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Andres, N","venue":null,"work_id":"193908b6-6f29-4f1c-9b49-f46d2204fb08","year":2014},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:c4e9fc4875b06834ec90f09b64c1a6a8f9f9a338c90f9d7a6b79c64bcd7be515","observation_id":"f3854e0f-b1e9-4eb4-bc70-aca4b23e68d0","resolution":{"observed_at":"2026-05-23T03:15:21.427325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1977","venue":null,"work_id":"27d90a37-695b-4fdf-8fe4-a281bc2634a7","year":1977},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:0cf405e0a6b9d09ad1032d8ac0ed917a6268419b388a11d6a945d476b9202ede","observation_id":"52f3bece-a523-4899-aedb-466dbc3d50ea","resolution":{"observed_at":"2026-05-23T03:15:21.420519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1985","venue":null,"work_id":"065d1045-25df-48ff-ac7a-6dffb4b40462","year":1985},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:9a7df4478173321b6e862f1d6e21d5ca33af7fef2aaa3d59a7e38682a474925b","observation_id":"1549f95e-2689-4d3b-a703-16b01b47b031","resolution":{"observed_at":"2026-05-23T03:15:21.459286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Tsai, W P","venue":null,"work_id":"beea045a-52ca-4b57-bcbb-0c98e51d572a","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:d174a62cdc3b6c5a362c1ca00fc3ff4ea1a6772e6e09de27fc7549d66cd222de","observation_id":"00e4e452-215f-46ed-beda-007c61dfca82","resolution":{"observed_at":"2026-05-23T03:15:21.413330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1995","venue":null,"work_id":"8a1446f4-b894-4b08-884c-74f16b298762","year":1995},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:d9fcc733d683cdc5435ffd17809afcc199ca425c8a53b374ed432efdf6f6b967","observation_id":"aaac8600-0ff7-4b25-80c5-2f71b2bfa67b","resolution":{"observed_at":"2026-05-23T03:15:21.465405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Graham, L","venue":null,"work_id":"3b87c36b-d504-4089-9931-0dc4676dc075","year":2015},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:47be4ed7ba6f2c40973e3ebdef33fd2d7767ca014af80a233defcd8f93fdb9cf","observation_id":"2a1a55cf-cd64-4e39-8c21-267b7c016b1c","resolution":{"observed_at":"2026-05-23T03:15:21.435996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Goswami, S","venue":null,"work_id":"03d3b0fe-0877-4dc4-b24a-682560a8561a","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:e3c93bec79e2fde38e7dae91982937a46c7cef6fd27f2ac3ed4e707f5bf3bdf0","observation_id":"40791481-9812-4c6b-9f83-31ad3941a3a6","resolution":{"observed_at":"2026-05-23T03:15:21.432947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Yulia, R","venue":null,"work_id":"9f522270-a90c-4ab2-9829-262be8164532","year":2018},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:c71ba5c63d22e415299cc3cd640977e9082a909c098bc0d09518a4633c98158f","observation_id":"02630060-b022-40bb-9c51-be17d11fae2e","resolution":{"observed_at":"2026-05-23T03:15:21.438975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/esp.4624","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DiBiase, R","venue":"Earth Surface Processes and Landforms","work_id":"d9958769-e587-4e2c-8657-74d0c5fb802f","year":2019},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:e87316692821441964a4dfa8e1a3969d90642b6cd80bb329b4b980a4bd2924cd","observation_id":"8c82b21d-0e5d-4007-ab7a-620b3af1067f","resolution":{"observed_at":"2026-05-23T03:15:20.981327Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 2008","venue":null,"work_id":"373ba7c0-781f-49b8-9a5f-b0040ee706a6","year":2008},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:36c8c33d3ef54ec2415ae827fe46d8fc4f047183fc4a6a6f24a7fb243b20a68b","observation_id":"55697ebe-9940-4b8e-988e-fc984087a885","resolution":{"observed_at":"2026-05-23T03:15:21.430068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1775","venue":null,"work_id":"d80d1349-052f-4b0d-a040-9307073ce401","year":1921},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:e165030a4526aa346917c5bbf36a77a0b2ba5ea32fc1714ffcb145b5acd3d167","observation_id":"6288c316-1cb9-400e-a356-577ca8a2f1b2","resolution":{"observed_at":"2026-05-23T03:15:21.456335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/aic.690190228","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"APACrefauthors \\ 1973","venue":"AIChE Journal","work_id":"f591c706-6811-4d44-9721-0e6c74a1f650","year":1973},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:e2504352f206520701c1d9764823b5ae5f32f21bca641fe8891b2505c90bc121","observation_id":"d54932b4-558c-474b-ac83-69bbf80a044b","resolution":{"observed_at":"2026-05-23T03:15:20.988327Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1937.0150","doi":"10.1098/rspa.1937.0150","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"\\ White, C M","venue":"Proceedings of the Royal Society of London A Mathematical and Physical Sciences","work_id":"81ff0e10-43d2-4cdb-9498-7f36c04b127c","year":1937},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:0a08ad3a8cd34e9e156379e01934f284fa8bdaaba4da6889521986e4c176d6d9","observation_id":"23142a85-a7b4-43e4-a607-73a24cba6143","resolution":{"observed_at":"2026-05-23T03:15:21.011848Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.7738525","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"\\ Rackauckas, C","venue":"Zenodo (CERN European Organization for Nuclear Research)","work_id":"4e4acf49-0473-476b-b329-4589a3a4d208","year":2023},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:9f8f388e340a4cec5633095253f3e9be37a584674ef28cb0abbd90bd2e1d97b8","observation_id":"b1584685-0145-4853-b7ea-e1504b179535","resolution":{"observed_at":"2026-05-23T03:15:21.007323Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5194/hess-27-2357-2023","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Beck, H","venue":"Hydrology and earth system sciences","work_id":"1d2bd503-69b6-4281-8dcc-06787db3d9fe","year":2023},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:fc0638312d86ad72276e28bf91b05a6d1d19e23fc82754c7f39d44d2c39e37ac","observation_id":"1cf96b56-b04e-490c-8f30-defd8c2ffaaa","resolution":{"observed_at":"2026-05-23T03:15:20.991353Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"\\ Maurel, F","venue":null,"work_id":"24b538d8-01ad-4d73-8e33-edcb94e230ef","year":1997},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:42424bdb40f02c150c02bcc5277acd73821b35df01cbbfc0d1246fe0763de832","observation_id":"9d11f102-0f95-44e9-9597-7cf6d9cde0b4","resolution":{"observed_at":"2026-05-23T03:15:21.518547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"cbdd124b-4390-48f1-8fe2-77ac54dcf0f8","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:1ea694b55e88719ee28b5ef3c351e9586e7309d88bfb731430acb36471a120b9","observation_id":"edcc17a6-7a08-4be1-93bb-ec3ddb069ab5","resolution":{"observed_at":"2026-05-23T03:15:21.554884Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 2015","venue":null,"work_id":"5fdb34ef-45a1-4b01-9886-fb9007d79f4c","year":2015},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:40e876386014e10560d88e891e814257772daf26cf0777c2758d8e7c08c2260a","observation_id":"6e40849c-c9bd-4632-a40c-c43bb5ae871a","resolution":{"observed_at":"2026-05-23T03:15:21.563785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"\\ Syme, B","venue":null,"work_id":"2ece8bdd-3b12-4b19-ad87-3a378be733ee","year":2016},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:a49be906764ded8405177269a42b3e0dcd7b64384f04fcec1b47eca91bc7c9ac","observation_id":"a51c7a8a-9c83-46e8-a0e1-4b0f1c2c9403","resolution":{"observed_at":"2026-05-23T03:15:21.462374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 2016","venue":null,"work_id":"5dd15b03-f117-40dd-a328-a29c32b7af1f","year":2016},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:30bd344e36999eb38cbfa3f0ff218b2e981df7ad9bd573f1e0d3906f6ed94101","observation_id":"f6ebbe3d-864c-46a0-a551-c945941522de","resolution":{"observed_at":"2026-05-23T03:15:21.445292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zheng, Y","venue":null,"work_id":"6d8fb680-6ccd-4220-8dc4-08af4fff43f1","year":2020},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:c4b018c5a36af8dcb4647e2a06705cb7b07f870fe336949e742084c6bf0f0565","observation_id":"79833481-4978-4f8e-96d1-62692a984799","resolution":{"observed_at":"2026-05-23T03:15:21.541572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 2010","venue":null,"work_id":"2404ee57-1802-487e-a821-93a97410d9b3","year":2010},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:19136412cdfae2ac29e9252dda3f6f959fbaba6469247ef72fed986691bb57a2","observation_id":"d2f8da5f-2ac8-4c24-b469-c4e63d6b5a98","resolution":{"observed_at":"2026-05-23T03:15:21.544405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1029/2017wr021649","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ghorbanidehno, H","venue":"Water Resources Research","work_id":"d31440ee-4134-404f-ab38-68f8fde47b74","year":2018},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:94d68d6e9e1acd8bec60c5af852431f3487842937fdb2af798e34b06b1ab4056","observation_id":"1897b783-59ae-4306-95d9-0620610d7327","resolution":{"observed_at":"2026-05-23T03:15:21.017020Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":{"arxiv_id":"2210.12375","last_updated":"2023-01-17T09:02:47Z","snapshot_observed_at":"2026-08-03T00:08:58.961595Z","submitted_at":"2022-10-22T07:08:17Z","title":"torchode: A Parallel ODE Solver for PyTorch","version":2},"cited_work":{"arxiv_id":"2210.12375","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.12375","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"\\ G \\\"u nnemann, S","venue":null,"work_id":"99d0fc00-ed38-4ba4-bd21-0a301b7e1728","year":2022},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"cited_paper":"/paper/2210.12375","citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:d63d3646ffd6c93d62a58cea859d7bd7178c3a613192c67d13fe849dcd57ad7c","observation_id":"1961071a-658a-4ed3-ab5a-15bf17b3e48d","resolution":{"observed_at":"2026-05-23T03:15:21.190329Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1970","venue":null,"work_id":"145ddda6-0be1-4814-8153-625f82d4b6e2","year":1970},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:17c76508bde9e5b101ac3d00ce08991f1affcf5402ae7609968a7953c5ff1743","observation_id":"ed5adcf8-99f5-4172-95d1-aac31b932694","resolution":{"observed_at":"2026-05-23T03:15:21.538096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 2025","venue":null,"work_id":"20d12806-c0bd-4816-8aef-4a9a28aeaa70","year":2025},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:64041e5a0f3f75db63cb68038fd826419f1d49b630cd6213ef710572d10e7d24","observation_id":"516ba153-c648-40cd-b9af-f7c16932adf7","resolution":{"observed_at":"2026-05-23T03:15:21.532212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Landry, B J","venue":null,"work_id":"35387fb4-c506-4ad3-80c5-25abb9519940","year":2008},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:f36b73593bf1ac92971dfcdb994cc153440baadc50f59b40b4c3828c29d94ea1","observation_id":"efeb5900-542c-4782-9a63-e83ea8ba2b0c","resolution":{"observed_at":"2026-05-23T03:15:21.529507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.17226/27730","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mazdeh, A","venue":"Transportation Research Board eBooks","work_id":"d78e4747-9457-44f2-ac1d-c83ca78f8dff","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:b9f696ff260fa514f044c24f7dc805122e81d11f4304f0b61e22a63079427bf1","observation_id":"f01586c8-c4cc-4e9d-a43d-a68ee6cc1098","resolution":{"observed_at":"2026-05-23T03:15:21.014414Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Shen, C","venue":null,"work_id":"f8db71b4-a4fe-40fb-8ba2-ed436cf757d1","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:937d420c4f43d08a849fc568467db23e948050d7aadfa480283a239cb7a2a226","observation_id":"7bd449b6-0ab5-404f-a4e9-14fddf2af076","resolution":{"observed_at":"2026-05-23T03:15:21.534881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1891","venue":null,"work_id":"c7b4c9cb-9e0d-4aee-9231-b6176872270a","year":null},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:93fb0c89da2d68036c31fb80537d8353ec42a6b61be6d858e4165c2332b5c7f0","observation_id":"adca9e34-af6f-41af-af2d-e989e37ab957","resolution":{"observed_at":"2026-05-23T03:15:21.547759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Roberts, K","venue":null,"work_id":"ab786794-b875-4405-91b1-b1fbe62e73bd","year":1992},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:32d7deddae5d3398f72f6fe2fb65b48d654490a222f1af997dee177526dcfda1","observation_id":"693cd0cf-2af1-45a8-b2bc-4385bcf75ad7","resolution":{"observed_at":"2026-05-23T03:15:21.557606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pereira, F","venue":null,"work_id":"f805bb71-55d4-4087-a787-37c368eee6c8","year":2018},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:e0c39378ac7c9b8f5ce912940c513dea75973f62b46a39e9d664a5e3d8ce29e6","observation_id":"ee55a542-3e91-4471-b4e2-0bd7bc8188c6","resolution":{"observed_at":"2026-05-23T03:15:21.448036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Saki, S","venue":null,"work_id":"8ccdac91-1a69-42d9-9f1b-ff1d46ec896c","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:731f223849cd512db2fd403dd63a602ea1b002ef92dc4e5e3ff667959f4f0420","observation_id":"4afc2e4f-418f-478b-a538-2f0a7644c59d","resolution":{"observed_at":"2026-05-23T03:15:21.442028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1944","venue":null,"work_id":"1709afaf-405e-4fd5-bfa2-e86cccc329f1","year":1944},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:bf93c68b2dc4ae25770fe6e31661ed75dc3996bccba94c43068ced5e8a24d07b","observation_id":"098b485b-628a-4c98-a4eb-e742ebaf4b9b","resolution":{"observed_at":"2026-05-23T03:15:21.523701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"o mungsgesetze in rauhen R ohren Str\\","venue":null,"work_id":"f293d193-e2cc-4f88-bd8d-406bca835a45","year":1933},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:71dcc845ba099e2218e2cd1b647457b8e600f728ffbc85d8447a731fb44a61eb","observation_id":"039f83d5-4049-4d55-88a1-319705d681da","resolution":{"observed_at":"2026-05-23T03:15:21.526693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1950","venue":null,"work_id":"0c9c606a-c625-492c-adef-c5626fd7a398","year":1950},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:a10dfa887a195df1313ab28016cf58a9871a1e6ccc6087236c4d9fca9af54c2e","observation_id":"e4ff40a1-ee66-4247-9089-a5556c586040","resolution":{"observed_at":"2026-05-23T03:15:21.560371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 2011","venue":null,"work_id":"93e9cf96-fc43-4db6-9ca6-a98b89347f4e","year":2011},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:c397581823d863430b62f3f3b82169fac46fc773c618547e887d5cf4c61ac44d","observation_id":"9a09d6b3-aacb-476a-b79f-655b304ec60b","resolution":{"observed_at":"2026-05-23T03:15:21.566735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Moteki, D","venue":null,"work_id":"ff6a3590-8dcb-4cc8-b0b6-0f602eff8112","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:3126c585e167961253b90b64f322d1a3f3ca78662757368f1bac45229faca095","observation_id":"5d3d7311-2925-41de-9928-c76f3f3b5317","resolution":{"observed_at":"2026-05-23T03:15:21.417051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2005.13420","last_updated":"2020-07-31T00:28:12Z","snapshot_observed_at":"2026-07-06T09:23:49.227827Z","submitted_at":"2020-05-27T15:28:11Z","title":"Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows","version":2},"cited_work":{"arxiv_id":"2005.13420","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2005.13420","snapshot_observed_at":"2026-07-04T13:09:51.370938Z","title":"\\ Ruthotto, L","venue":null,"work_id":"21996863-0960-40ac-b4fb-14bae5972f20","year":2005},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"cited_paper":"/paper/2005.13420","citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:dcd97f0135840d3c4b50bcec416fe2fb80096490224bb66805fbb0ba6c21bfa0","observation_id":"891917fe-47d1-4e58-a769-c8b3d75ca2aa","resolution":{"observed_at":"2026-05-23T03:15:21.195369Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04385","last_updated":"2021-11-02T12:06:44Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-01-13T16:40:35Z","title":"Universal Differential Equations for Scientific Machine Learning","version":4},"cited_work":{"arxiv_id":"2001.04385","doi":"10.48550/arxiv.2001.04385","metadata_source":"pith","pith_arxiv_id":"2001.04385","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Universal Differential Equations for Scientific Machine Learning","venue":"cs.LG","work_id":"378f38aa-6e4c-4bad-9b0e-22dc33841b07","year":2020},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"cited_paper":"/paper/2001.04385","citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:2d2f940d56a794563a1b7b8c11e78c93eee43b19fb46ac47938d54aac0ba4ec4","observation_id":"1205aebd-a0d1-4d8c-a111-d0ea3a716003","resolution":{"observed_at":"2026-05-23T03:15:21.184661Z","resolver_source":"local_arxiv","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":"2026-08-03T19:39:19.041277+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-03T19:39:19.041277+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"\\ Nie, Q","venue":null,"work_id":"196cd561-8bca-4e93-8656-0ad449d14723","year":2017},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:da795020d6a9ed9dc9b1857c9a2641dbde0ae00d58e4f649645c488c4a614195","observation_id":"832277e0-acee-4af3-b6bd-120f1067c5ed","resolution":{"observed_at":"2026-05-23T03:15:21.552162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Appling, A","venue":null,"work_id":"bf4c1b89-fa0f-43e4-a33e-046126bc33c9","year":2023},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:97a477edc2bde4f0be4b74b2fe3280c01d0a3694083c8e2ce6f180924b52c817","observation_id":"297a4183-cc48-4978-a16c-0f6d7c38d3c7","resolution":{"observed_at":"2026-05-23T03:15:21.515279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Perdikaris, P","venue":null,"work_id":"c1035611-35c5-4ddf-8509-cdd909daee03","year":2019},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:535a3756ff5f12d8e0ea21f8f3100af2d9ab86eb0fc44c1c5593ae667aded3e7","observation_id":"7e9884e8-4723-4561-ab67-068695d7956e","resolution":{"observed_at":"2026-05-23T03:15:21.521206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.jcp.2003.07.020","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Borthwick, A G","venue":"Journal of Computational Physics","work_id":"e5f074dd-652d-4d30-928a-4e127b124529","year":2003},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:79174a4d6fa828e9c04d1a29e7902c050b5b12094916d123358760799a3841f4","observation_id":"0cdf88af-d2a0-4a00-abdd-ca6b8b62d6d0","resolution":{"observed_at":"2026-05-23T03:15:20.984437Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/1097-0363","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Fujihara, M","venue":"University of Hertfordshire Research Archive (University of Hertfordshire)","work_id":"d6714d8a-6cc2-433a-ad4f-2e77acdab5db","year":2001},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:b471ba2bb757b9e69167beebf3146e2677d516ae997edb32c5c747b7483b4d4a","observation_id":"0a490ab9-b5bb-4ab0-a5f9-429c3e79263c","resolution":{"observed_at":"2026-05-23T03:15:21.004355Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1965","venue":null,"work_id":"9c9e565b-e6c9-4a15-a5af-ae9e53a7512d","year":1965},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:4fa626f1517e07dd567b40da825b2151775bc00f54b2330906ab3ca5e81e539f","observation_id":"9554b513-6323-41f8-a659-f2c1d9395cfc","resolution":{"observed_at":"2026-05-23T03:15:21.506538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bolibar, J","venue":null,"work_id":"3628b264-fe1e-4ead-8cf8-24abbcc32d40","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:2fbade69dd8e998f95ba939269d847ed529848c02d14f4587f5cdf3672771b67","observation_id":"2c81961f-407b-4f40-ab56-6959d179b4e5","resolution":{"observed_at":"2026-05-23T03:15:21.453624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1991","venue":null,"work_id":"824591c9-1290-4b17-96ab-4ad258449555","year":1991},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:f1d1a1ed992bc6d0261f39af91f648a04944ddca52a739eda419bb66b8c4aad0","observation_id":"0bbee144-3028-4172-a4f2-f2e58852d40a","resolution":{"observed_at":"2026-05-23T03:15:21.503673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s43017-023-00450-9","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Appling, A P","venue":"Nature Reviews Earth & Environment","work_id":"d409f099-fa0f-4219-bade-81b89ee11424","year":2023},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:a498bdd1eed5bf69e3c752d4b3913cbdd108fd5962a2954fd617a6c3f35c0987","observation_id":"2cb204f2-a79e-4634-992f-3acebabfcc6b","resolution":{"observed_at":"2026-05-23T03:15:20.994027Z","resolver_source":"doi","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":"2026-07-20T15:52:48.183384+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-20T15:52:48.183384+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.advwatres.2010.09.002","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"\\ Phanikumar, M S","venue":"Advances in Water Resources","work_id":"276358c3-1ed3-47ed-895e-e23ed645534e","year":2010},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:10fc61ce6eafc4f73ba71a18494c601273da2e590e198ce0dff9f2b696ce33ec","observation_id":"9d86da7b-4870-4213-8a02-94bc4fc06ec5","resolution":{"observed_at":"2026-05-23T03:15:20.997972Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Le Maitre, O","venue":null,"work_id":"ef125b85-fbfe-4f09-9b04-6a387412070e","year":2020},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:53c2069bf74d653b9511df118f8e044e7d150a8a670401d86b4df5be12324380","observation_id":"a9351110-c0b4-4f89-a827-e1684c47c930","resolution":{"observed_at":"2026-05-23T03:15:21.497977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mayo, T","venue":null,"work_id":"b632b3f9-b416-4282-a599-6fd18b85b81b","year":2018},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:2f7b9e5e5126ae2c3c2177b785b79bf97d719d96dd130723a9daa37e764ec7f7","observation_id":"08392882-40ba-4265-8a7b-4810354f3e2f","resolution":{"observed_at":"2026-05-23T03:15:21.450794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bindas, T","venue":null,"work_id":"e15cc338-09d7-4299-9283-10d33eb217f4","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:fd91026223cedfad63653a12b5487f0ddeaa9ca97e67e63fdd13bd436d024daf","observation_id":"005bd7c1-a60f-411a-8606-4fa155420a2e","resolution":{"observed_at":"2026-05-23T03:15:21.495139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Knoben, W J","venue":null,"work_id":"80720794-cc3c-4305-929b-42070abc5ab1","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:1a1ee8804db381ea66ea107ed01cb338ea1857e43fb9e62a89dfdbedc4488fb1","observation_id":"41054173-4f71-40e5-bbaa-d31887cf2f5b","resolution":{"observed_at":"2026-05-23T03:15:21.500768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sawadekar, K","venue":null,"work_id":"d47df10d-8693-4242-89cc-6b719de6996d","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:f943f020f1a332769cef7fe68d0bcc239428848b17e5b9957f75c00346c612da","observation_id":"5606f130-33f6-494e-bf01-40d035d67a3b","resolution":{"observed_at":"2026-05-23T03:15:21.509479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Shen, C","venue":null,"work_id":"33d5f72a-8435-46f4-90a4-8990ad10e99f","year":2023},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:899f326eff872420d71c2a0fcfd9a88565b8722507fc9e1481020914ce92c063","observation_id":"d43a5d90-4bd3-40c3-a9ab-7da2ff2a0f80","resolution":{"observed_at":"2026-05-23T03:15:21.512366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 2022","venue":null,"work_id":"664a29d0-703b-4e57-8427-836104f1882e","year":2022},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:cd81ebeeb305cf8f1a0f58e04c542a920c558ebb15e9b5596c90ea3e89b91158","observation_id":"c524920d-c9bf-4256-b576-23042f2334ef","resolution":{"observed_at":"2026-05-23T03:15:21.489693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.camwa.2011.06.002","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Runge–Kutta pairs of order 5(4) satisfying only the first column simplifying assumption","venue":"Computers & Mathematics with Applications","work_id":"67d4ecb9-4dc6-4e0e-992e-b318ffb28296","year":2011},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:96dcf19f60467db58b80e10b805149a70a8b9eab53b7bc14e09cf3b3d83959c7","observation_id":"5e36ca4f-3087-440f-97a3-9612a2e625ba","resolution":{"observed_at":"2026-05-23T03:15:21.001414Z","resolver_source":"doi","status":"metadata_mismatch"},"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":"2026-08-06T20:38:13.868247+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-06T20:38:13.868247+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Younis, J","venue":null,"work_id":"4ce61ce7-cd99-45c3-8928-be3c75fb3ede","year":2010},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:751ac67962534cafcdc0db099316070d908f0d40f8202455461ea4d013500972","observation_id":"fbf0fe18-243b-4c75-bed7-3fa1257d3095","resolution":{"observed_at":"2026-05-23T03:15:21.483350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Jiang, S","venue":null,"work_id":"d40dde9b-06aa-4088-af8d-665a72ac0be4","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:c76db1e5f19ec300cd107801717db5aa96631dde11dbedf315b9fab254171d75","observation_id":"02d0436c-fdd1-477f-803b-f4627be7d0da","resolution":{"observed_at":"2026-05-23T03:15:21.486378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"\\ Bach, H","venue":null,"work_id":"74cb1f22-9b2b-435a-a704-e18896cbc50e","year":1992},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:6bafa53bcb95bf20df521642532761ee25b0ed6a61140b6bf03d6a3deddfa6d3","observation_id":"0599f1cd-af4a-4449-9e44-61b94d13bb19","resolution":{"observed_at":"2026-05-23T03:15:21.492310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zhang, J","venue":null,"work_id":"68c30c1c-f2bb-4f80-809b-aeb2b4115523","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:0f2b2d46a57a0186bc00dce5259e6032fae9095ab37fccde3f89b3bd26abeb6a","observation_id":"2580bd8a-44bc-411f-96f0-6168a049dc3d","resolution":{"observed_at":"2026-05-23T03:15:21.477650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lehman, W","venue":null,"work_id":"6d4aa1c8-029b-47e9-b4c7-14e06ca169ae","year":2022},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:3b73c87efd6a0162989e2862972fb8287df33977c98ed3953bfebb8295f252e5","observation_id":"ef149a80-d9ce-427c-baec-e38a8d6c4526","resolution":{"observed_at":"2026-05-23T03:15:21.480531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 1992","venue":null,"work_id":"4611af8f-9365-43f9-b63c-7b08964acba3","year":1992},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:764be052dc51b7035f2191350218a0745dc2c7f06ffb4fcc5dc52d37e59068f8","observation_id":"0b7b363b-ab2d-4a63-b593-ad66c3aab484","resolution":{"observed_at":"2026-05-23T03:15:21.471572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"APACrefauthors \\ 2002","venue":null,"work_id":"0791cd46-cf26-464e-b9c3-8d16d6f02f75","year":2002},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:86cbce14ec957ee0c7a542c94fccd9f3f6f36403ebd31445cec7213464a6e578","observation_id":"0aee79c0-c52a-4330-b378-262066cdedc7","resolution":{"observed_at":"2026-05-23T03:15:21.468403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lei, H","venue":null,"work_id":"f7efa825-2411-48a1-8f1b-de96ae4621f4","year":2024},"citing_paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-05-23T03:15:12.654574Z"},"links":{"citing_paper":"/paper/2502.12396"},"observation_digest":"sha256:c8dfba37ba250f004b8850a2974a3bee347343ca95918ad4043bc9883714b3a1","observation_id":"938bb49a-96a3-4f10-9011-7b3875e8a00b","resolution":{"observed_at":"2026-05-23T03:15:21.474846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2502.12396","last_updated":"2025-02-18T00:07:14Z","latest_version":1,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-07-06T20:38:15.649336Z","submitted_at":"2025-02-18T00:07:14Z","title":"Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming"},"reference_resolution":{"displayed":68,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":1,"verified_exact":14,"verified_fuzzy":51},"total_outbound_references":68},"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 7 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2502.12396."}