{"as_of":"2026-08-14T21:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:52c8c77d162fc6b665b75a5a4c88ac5a425d74dc54359f46042befdaf649b005","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T20:09:09.897484Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2411.10010/citation-record","integrity":"/paper/2411.10010/integrity","json":"/paper/2411.10010/citation-record.json","paper":"/paper/2411.10010"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.533422Z","title":null,"venue":null,"work_id":"0e9f4274-3ea8-43de-a970-92039f4ef3be","year":2011},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.638823Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:48b65202c856a995e267a73aa3bdd6cfecc72fb29939027bd9c7f0fcc85cbfb1","observation_id":"bda9a5e5-5b3e-4916-9720-56c8f6686d5d","resolution":{"observed_at":"2026-08-12T20:09:23.536601Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.02556","last_updated":"2022-11-03T17:19:43Z","snapshot_observed_at":"2026-08-13T13:49:54.458230Z","submitted_at":"2022-11-03T17:19:43Z","title":"Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.02556","snapshot_observed_at":"2026-08-12T20:09:09.645299Z","title":"Xie,H.Zhang, X.Chen, X.Gu, and Q.Tian, 2022: Pangu-Weather: A 3D High-Resolution System for Fast and Accurate Global Weather Forecast","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.645299Z"},"links":{"cited_paper":"/paper/2211.02556","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:d7b3243ec8033f07a29521ab5b90b37078634a55ac7e88dd002ced12862399da","observation_id":"1ee51c4e-968f-4c9a-ab1b-c707b65c7d4a","resolution":{"observed_at":"2026-08-12T20:09:09.645299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:09.678813Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.678813Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:b4c9a7cd1be18fec52deb0ff7de9a772768d3f5c11ca5036bb3c0f72ad0d12c6","observation_id":"b448d9ac-f2ef-46dd-816c-dd98125f58a8","resolution":{"observed_at":"2026-08-12T20:09:09.678813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13063","last_updated":"2024-11-21T20:14:58Z","snapshot_observed_at":"2026-08-14T11:07:15.764758Z","submitted_at":"2024-05-20T14:45:18Z","title":"A Foundation Model for the Earth System","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13063","snapshot_observed_at":"2026-08-12T20:09:09.724363Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.724363Z"},"links":{"cited_paper":"/paper/2405.13063","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:78daf70f90d52112cd79790b8aed67f8639ff03204c04ad5dbc2a2a08c0bd4e5","observation_id":"776cb724-47c3-4654-9ee5-f5cd3225b928","resolution":{"observed_at":"2026-08-12T20:09:09.724363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.03838","last_updated":"2023-06-06T16:27:17Z","snapshot_observed_at":"2026-08-14T14:08:47.408232Z","submitted_at":"2023-06-06T16:27:17Z","title":"Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.03838","snapshot_observed_at":"2026-08-12T20:09:09.747691Z","title":"Kurth, C","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.747691Z"},"links":{"cited_paper":"/paper/2306.03838","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:d9643e42b6524442e5fb9aecb537d6e79451d68e2ccdeb35fc59b394a3cad262","observation_id":"d98b556b-7670-46ea-a6e0-156a847bbf37","resolution":{"observed_at":"2026-08-12T20:09:09.747691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.523031Z","title":"Milton, M","venue":null,"work_id":"41350491-5a55-4e2d-be21-49f37de6188d","year":2012},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.751982Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:7e53982d3e4a59e40667dee96e040cc01163f2cf6174ff76ff307650cb57540b","observation_id":"79cd6682-18b4-4451-9498-ef255ecd7030","resolution":{"observed_at":"2026-08-12T20:09:23.525866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.512458Z","title":"Reinhart, and J","venue":null,"work_id":"a2268d7c-e84b-4e3b-bd74-e59a03aa536a","year":2023},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.756636Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:b37ef815c8870ff5eaa199a112e4114f07875da37a692120ff992e7f0c995a22","observation_id":"a6ef15eb-bb27-4af2-a643-94741f759585","resolution":{"observed_at":"2026-08-12T20:09:23.516083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:09.760639Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.760639Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:0b11eef6ebbd68691fb741be08370e8d5da732da4b5ae4fd34585133907cfc2c","observation_id":"6650b3ff-c575-4de9-8937-d05cdc9949ab","resolution":{"observed_at":"2026-08-12T20:09:09.760639Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.501956Z","title":null,"venue":null,"work_id":"7c8a5807-cb93-4f5b-8ca2-1bb3401483d9","year":2009},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.763936Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:ca7cb7a7190542a5b9770f7fd3a3c41447846c14493a8fb56e5f118d7a39e6a4","observation_id":"cacee5b3-15a2-4980-a25c-6eb04185ca5e","resolution":{"observed_at":"2026-08-12T20:09:23.505660Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-13T14:19:26.598265Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-12T20:09:09.767611Z","title":"Beyer, A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.767611Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:aef3a053aeffe45abd48d9e93059f1c9ec2f6d2d12acaadd79efcf5d93f32084","observation_id":"3b6a6614-1ae4-4f5e-9034-0724d5b9a704","resolution":{"observed_at":"2026-08-12T20:09:09.767611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:09.771424Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.771424Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:78364112e4c31276dd487082b993f0b35c4e0431c0346dcb16e5e588a0056ea9","observation_id":"561214b5-b98d-4350-a89d-2dd32a759744","resolution":{"observed_at":"2026-08-12T20:09:09.771424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2151/jmsj.2024-003","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:09.939563Z","title":null,"venue":null,"work_id":"a7aa4739-a2a8-4081-9843-01af434debbc","year":2024},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.774935Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:1089599b2e599cd7bd795fdecddd85b41ba0c2acc9d062b584fc7856fa4ec9ea","observation_id":"f3f78580-38d6-4dfb-8f44-46a4efda7f36","resolution":{"observed_at":"2026-08-12T20:09:09.944015Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.492251Z","title":"European Centre for Medium-Range Weather Forecasts","venue":null,"work_id":"de40fab0-97d8-479e-a4b0-71a7c265c9eb","year":2024},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.778721Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:7f3213d899d6d1c89c7d75f528743a7239f9ed81fd417c169a3bcf2a100809bb","observation_id":"4ac7fd27-e80f-4e7f-a8fb-d89df9ce9e5c","resolution":{"observed_at":"2026-08-12T20:09:23.495743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.481995Z","title":null,"venue":null,"work_id":"29aa98fb-6b8e-4c06-828c-8cb1db8ed242","year":2011},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.781915Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:787ef3f25b292b0b259e706f31afa8645cd8dfda15404ac20617a031a5c54e19","observation_id":"e965e317-cfe8-4c33-bc6d-ff31976939ab","resolution":{"observed_at":"2026-08-12T20:09:23.486027Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.471825Z","title":null,"venue":null,"work_id":"8c7f3ba8-51b4-4ffa-aa1f-aa36b795f3fc","year":2017},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.785501Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:dfc684a8b70fa532deeb0865d06a83a08df902751782ccd62f4f4eb4d77df1ab","observation_id":"91e81ce5-5b0c-4be3-aacd-1a8e98e2b957","resolution":{"observed_at":"2026-08-12T20:09:23.475590Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.461343Z","title":null,"venue":null,"work_id":"00ef868b-3cc4-437b-999c-eb3bfdb96955","year":2017},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.788750Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:792273ca6101835d4c2d9c2b7eb266c0c52d242f916fa86ccbe093031c13720c","observation_id":"793b3a43-5a88-43f0-a2c8-9b33e7faa3ed","resolution":{"observed_at":"2026-08-12T20:09:23.465085Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00059","last_updated":"2024-01-28T13:23:25Z","snapshot_observed_at":"2026-08-14T16:47:36.733556Z","submitted_at":"2024-01-28T13:23:25Z","title":"FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00059","snapshot_observed_at":"2026-08-12T20:09:09.792676Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.792676Z"},"links":{"cited_paper":"/paper/2402.00059","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:cb968a0fa0ea3bf483b3248190286395b10310dff76be6ddf3e878906ec410ff","observation_id":"58f5e7bb-bd8c-4d87-a9dc-fef04fdc1070","resolution":{"observed_at":"2026-08-12T20:09:09.792676Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11239","last_updated":"2020-12-16T21:15:05Z","snapshot_observed_at":"2026-08-10T05:21:27.485481Z","submitted_at":"2020-06-19T17:24:44Z","title":"Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11239","snapshot_observed_at":"2026-08-12T20:09:09.796009Z","title":"Jain, and P","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.796009Z"},"links":{"cited_paper":"/paper/2006.11239","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:3a572898f22b1de56505010d113eda208bb834027c41a9a48421d2eb97ffe7bc","observation_id":"674deaf8-22c1-415e-8924-f6cf35b22361","resolution":{"observed_at":"2026-08-12T20:09:09.796009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.450413Z","title":"International Civil Aviation Organization, 16pp","venue":null,"work_id":"f3c99a57-6732-4c67-a253-bfe8e7473627","year":2016},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.799836Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:9977b4789d559f3e97f9f5c698b9428931860d067295022ccbaa378089b0348f","observation_id":"70385672-e622-4051-b1f8-2f318c388aaf","resolution":{"observed_at":"2026-08-12T20:09:23.454123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.439899Z","title":null,"venue":null,"work_id":"4aae97c4-c82b-4cd0-b061-5e00c83ec12a","year":2023},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.803545Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:d930bbbf2f2c74297b11682838330c69c3aee59a3bf08e714385b907eef18eb6","observation_id":"4a088570-17ca-42d5-8c0c-c9fc2655f088","resolution":{"observed_at":"2026-08-12T20:09:23.443837Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.428694Z","title":"Report of Numerical Prediction Division, 64, 248 pp (in Japanese)","venue":null,"work_id":"85b615f8-9bf8-4c20-9721-71ba25d4acc3","year":2018},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.806798Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:3b6089419b7d3424de31896a3e7186c375df8314a4dd617b4f9aa4877d79c0ba","observation_id":"06f539b5-6e70-409e-a95f-f6da02db8831","resolution":{"observed_at":"2026-08-12T20:09:23.433355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.416681Z","title":"Japan Meteorological Agency, 143pp","venue":null,"work_id":"cd4f8c56-09da-42ac-873e-3226b3a1cf3e","year":2022},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.810013Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:1a45165e1e9912e4d3ec2045da8d26450df93ad6b6cc3f46b6623424a5109adb","observation_id":"44f43f41-5aae-4887-8e0b-ed5530ac48f2","resolution":{"observed_at":"2026-08-12T20:09:23.420722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.405371Z","title":"Japan Meteorological Agency, 262pp","venue":null,"work_id":"f113a3c2-e4b6-470f-a528-0791450fe756","year":2024},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.813151Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:3abe5497efe35621e366bc51cdeef6c7f4897d849071410b65e884b1af4e4deb","observation_id":"ab7f3e9c-eeb2-4b56-a45a-0039aad04b56","resolution":{"observed_at":"2026-08-12T20:09:23.409177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-12T20:09:09.816235Z","title":"P., and J","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.816235Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:237bea259d9575954daf29f9303f63fd95b5b742e9c9dd609eb8eb62f0d17693","observation_id":"642c4c0b-71d1-40f4-9c65-0877b1cceb27","resolution":{"observed_at":"2026-08-12T20:09:09.816235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.12794","last_updated":"2023-08-04T17:07:43Z","snapshot_observed_at":"2026-08-14T11:07:21.792512Z","submitted_at":"2022-12-24T18:15:39Z","title":"GraphCast: Learning skillful medium-range global weather forecasting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.12794","snapshot_observed_at":"2026-08-12T20:09:09.819813Z","title":"Sanchez-Gonzalez, M","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.819813Z"},"links":{"cited_paper":"/paper/2212.12794","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:832d5f6c89e6cfc236decc8ae8d570c5a5eb980504ab69ddcadf3b7c9c6d5aac","observation_id":"9a3cb690-9ddc-48e9-a6fe-1465dd9f466c","resolution":{"observed_at":"2026-08-12T20:09:09.819813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01465","last_updated":"2024-08-07T12:02:35Z","snapshot_observed_at":"2026-08-12T23:51:25.199336Z","submitted_at":"2024-06-03T15:55:10Z","title":"AIFS -- ECMWF's data-driven forecasting system","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01465","snapshot_observed_at":"2026-08-12T20:09:09.823201Z","title":"Alexe, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.823201Z"},"links":{"cited_paper":"/paper/2406.01465","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:e29954389ba6d5f2d08c125dee62e349a4144efe130b3120028082a48776d411","observation_id":"0024121d-f803-4bf0-a599-c2363951d140","resolution":{"observed_at":"2026-08-12T20:09:09.823201Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.394392Z","title":"Bengio, and G","venue":null,"work_id":"8991a28d-1b66-4397-b0f0-62d4ecc7aa50","year":2015},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.827404Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:39d621fc7de75af3c5281429e7852e07b5b1f0e4a38a954ba543806102f98956","observation_id":"345d1e0a-474c-418b-b55e-cd332c735a67","resolution":{"observed_at":"2026-08-12T20:09:23.398050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.382615Z","title":"Tantet, R","venue":null,"work_id":"081ae9ed-775b-4c74-9ae1-84ced7b6320b","year":2023},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.831956Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:3158802b4ac36c7915ddc0fe2cde533f9a32e2541e3f0e67209fe813331ea209","observation_id":"27087c90-b023-4bbe-9a0b-11cece98e119","resolution":{"observed_at":"2026-08-12T20:09:23.387104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.371525Z","title":"J., 1997: A Convexity Principle for Interacting Gases","venue":null,"work_id":"613eb985-0154-44cd-9649-5d6b1d3f050b","year":1997},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.835869Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:6feff2ec6137a60119ece2d2f0c669fcc780387587daeaa23fd479337c0c4d2d","observation_id":"c73940ec-5a03-4644-a224-1eb3bc3e18e1","resolution":{"observed_at":"2026-08-12T20:09:23.375383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.359301Z","title":null,"venue":null,"work_id":"b20deaa7-771a-4cb3-ac9f-b5894fefbf2e","year":2010},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.839364Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:b0ffba226b711360d54a0896527d67334ed15888fa63f2749881a816bbbe81a5","observation_id":"cb1ff268-128f-4692-bd72-644105363f94","resolution":{"observed_at":"2026-08-12T20:09:23.363673Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.346991Z","title":"[Available at https://vlab.noaa.gov/ web/gfs/documentation.]","venue":null,"work_id":"f84823db-5ce6-4cba-9c53-26ab48e72bc8","year":2016},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.842883Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:dd204515de1f60f8a4e7a7dba6bc4322e83bdd69c307e1bff29bccabb4517e19","observation_id":"d4c1b0ff-9dc3-40ab-99a6-2d57c1661c1a","resolution":{"observed_at":"2026-08-12T20:09:23.351460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.334600Z","title":"Fukuda, 2019: Advancement of Tropical Cyclone Track Forecasts","venue":null,"work_id":"a4a32371-7ef1-43ab-8c54-9e54e45b8599","year":2019},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.846349Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:a2a63b57ff0a8f130dc41c2925158288fa1547dd3a759ac4d2eba8deddc4daeb","observation_id":"dce09922-e660-450a-8bd2-6cfa34e125aa","resolution":{"observed_at":"2026-08-12T20:09:23.338708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.11214","last_updated":"2022-02-22T22:19:35Z","snapshot_observed_at":"2026-08-14T08:28:25.306803Z","submitted_at":"2022-02-22T22:19:35Z","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.11214","snapshot_observed_at":"2026-08-12T20:09:09.850149Z","title":"Subramanian, P","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.850149Z"},"links":{"cited_paper":"/paper/2202.11214","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:50e40025e51b2f26eaf4dc6d5544e9ea734214c75421c611a5e5fae89532bb10","observation_id":"fb98c655-1257-484d-814a-1d2592a00b9c","resolution":{"observed_at":"2026-08-12T20:09:09.850149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.00567","last_updated":"2020-03-18T09:54:55Z","snapshot_observed_at":"2026-08-14T19:40:34.002536Z","submitted_at":"2018-03-01T18:28:43Z","title":"Computational Optimal Transport","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.00567","snapshot_observed_at":"2026-08-12T20:09:09.853893Z","title":"Cuturi, 2020: Computational Optimal Transport","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.853893Z"},"links":{"cited_paper":"/paper/1803.00567","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:36b84e4552eae67756955c9b8e8ae289caac05d1535cb1f2a6db949534a93828","observation_id":"3de76272-5fc6-4abe-b3a9-d5910b8d6147","resolution":{"observed_at":"2026-08-12T20:09:09.853893Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.15796","last_updated":"2024-05-01T16:30:43Z","snapshot_observed_at":"2026-08-13T04:54:09.114334Z","submitted_at":"2023-12-25T19:30:06Z","title":"GenCast: Diffusion-based ensemble forecasting for medium-range weather","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.15796","snapshot_observed_at":"2026-08-12T20:09:09.857446Z","title":"Sanchez-Gonzalez, F","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.857446Z"},"links":{"cited_paper":"/paper/2312.15796","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:4ef9e0970b8fe327bb6301a5c4aa68e68dad74c2d0bc39415c72f5f94673174f","observation_id":"c79b23c3-43ef-4f79-a815-6a00089ec7d6","resolution":{"observed_at":"2026-08-12T20:09:09.857446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.11896","last_updated":"2024-01-23T16:08:24Z","snapshot_observed_at":"2026-08-14T14:14:55.168011Z","submitted_at":"2024-01-22T12:46:18Z","title":"Comparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.11896","snapshot_observed_at":"2026-08-12T20:09:09.862330Z","title":"Schulz, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.862330Z"},"links":{"cited_paper":"/paper/2401.11896","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:ff0b3a708835f66fcb89a79045f0348093d5a4118b9d1af96f08aec66459ec6c","observation_id":"1842e2d8-b53b-41ff-8d7c-1fa7011ff15d","resolution":{"observed_at":"2026-08-12T20:09:09.862330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.324295Z","title":"Benjamin, E","venue":null,"work_id":"9e457509-2b77-4c04-a1fd-6388be0b32ad","year":2023},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.866537Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:be4e8b3926fede2f33b9b7181f6fe6ab8e7818daffc06493345ef02b9046a93b","observation_id":"b34cf20e-7929-465a-a3f4-3133b5ebeb8a","resolution":{"observed_at":"2026-08-12T20:09:23.327802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1505.04597","last_updated":"2015-05-18T11:28:37Z","snapshot_observed_at":"2026-08-14T05:43:53.421538Z","submitted_at":"2015-05-18T11:28:37Z","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.04597","snapshot_observed_at":"2026-08-12T20:09:09.870225Z","title":"Fischer, and T","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.870225Z"},"links":{"cited_paper":"/paper/1505.04597","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:f3b58e3f2a8480730f0843ba953b8b15ed7beaa036fa431c14c8b9e64309f9bd","observation_id":"ab9c29f3-121f-471c-8c96-443d6584f084","resolution":{"observed_at":"2026-08-12T20:09:09.870225Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1175/waf-d-17-0068.1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:09.925157Z","title":null,"venue":null,"work_id":"182e70b0-bf3b-42f9-a347-106b4607d0a4","year":2018},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.874028Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:9b81e1fbd42c4b57a78321066a4240e27b972d51e69084849cad6cdf1e7e00d5","observation_id":"3fddf866-510c-4e28-8d4c-9625133f4c64","resolution":{"observed_at":"2026-08-12T20:09:09.930818Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.05600","last_updated":"2020-10-10T07:46:19Z","snapshot_observed_at":"2026-08-01T16:44:46.833530Z","submitted_at":"2019-07-12T07:37:26Z","title":"Generative Modeling by Estimating Gradients of the Data Distribution","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.05600","snapshot_observed_at":"2026-08-12T20:09:09.877649Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.877649Z"},"links":{"cited_paper":"/paper/1907.05600","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:c2a335f7d51551bfb863f6850ba00c88f804d1e7b8833ee7f0a2f2d6b61d07c7","observation_id":"268ce4cf-ea4e-45bc-9825-fca3ed837fbd","resolution":{"observed_at":"2026-08-12T20:09:09.877649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.313814Z","title":null,"venue":null,"work_id":"f0b90bf5-ce77-4bf6-a032-81dd537f293b","year":2021},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.881994Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:c58f9734d7780c13bc77fd108a930b891aabfbd8d5e671807e292643f4fe71f8","observation_id":"0893345f-85bf-4519-b44f-fcc95f6ed169","resolution":{"observed_at":"2026-08-12T20:09:23.317257Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-12T20:09:09.887787Z","title":"Shazeer, N","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.887787Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:8429d820dbad06b228f88889259d2c04a5730c2d4c51e4a5a74f8566ac5e5dd3","observation_id":"f38ea3a9-e1b2-453d-8ff0-0e0eb195d42d","resolution":{"observed_at":"2026-08-12T20:09:09.887787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.302035Z","title":"L., and J","venue":null,"work_id":"e028a0fd-ca23-469d-bafc-f0d7cdc9eed7","year":1997},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.893405Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:335b382ab5286961e5307f0f714660a81ec8e5a42ddbce79521320b74d14ae0b","observation_id":"9c85b2d1-5fd5-41aa-a88e-30f7a48990e7","resolution":{"observed_at":"2026-08-12T20:09:23.305618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T20:09:23.290439Z","title":"Bulletin nº, 62, 11–15","venue":null,"work_id":"e056b0d7-f12d-4bf4-86f7-d576d15ca3f4","year":2013},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.897484Z"},"links":{"citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:3884385a954568d5f1a0d501a9efe5a59b23b2afb33c0238a7739057d3886bd7","observation_id":"d595f240-f893-4ed8-b2e2-470e704a6942","resolution":{"observed_at":"2026-08-12T20:09:23.294110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T11:39:31.434484Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":27,"verified_exact":2,"verified_fuzzy":15},"total_outbound_references":44},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2411.10010."}