{"as_of":"2026-08-19T21:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:40409e0d490be120b5dd1047943d6ebed0a83c68e5b296a39f422accdfd07543","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T08:26:59.915573Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2510.20769/citation-record","integrity":"/paper/2510.20769/integrity","json":"/paper/2510.20769/citation-record.json","paper":"/paper/2510.20769"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T08:26:57.233922Z","title":"Gfs-powered machine learning weather prediction: A comparative study on training graphcast with noaa’s gdas data for global weather forecasts.Preprint, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:57.233922Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:c728ae4146743e4ff8d404e3497ce51e14d053ccd195d2d5e4dcd493f1abf3ad","observation_id":"e6541ceb-fcdd-48b4-9ac0-402109cd798b","resolution":{"observed_at":"2026-08-04T08:26:57.233922Z","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":"2026-08-04T08:26:57.300316Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:57.300316Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:ad3e308f7c6026a2502badfae797bf9f3ae1d60717015e9aa4e1d9106c2b9625","observation_id":"295fe7c0-d4d1-471f-9c54-322758c20aae","resolution":{"observed_at":"2026-08-04T08:26:57.300316Z","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":"2026-08-04T08:26:57.359593Z","title":"Fact sheet: Ensem- ble weather forecasting","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:57.359593Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:69ba227c9a439a3d63440e48186e8524a20287f3c4a74c1fd6a00737b3a8cc64","observation_id":"0a51fc37-b846-4566-9ad3-e778cb922d64","resolution":{"observed_at":"2026-08-04T08:26:57.359593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.02556","last_updated":"2022-11-03T17:19:43Z","snapshot_observed_at":"2026-08-16T16:18:47.978288Z","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":"2026-08-04T08:26:57.415214Z","title":"Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast.arXiv preprint arXiv:2211.02556, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:57.415214Z"},"links":{"cited_paper":"/paper/2211.02556","citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:28e21793be4424a30c640cd0b04ed52ef188c3bc4358109be739b914d68be1e4","observation_id":"6fc52804-c92b-45c1-a998-cc8dda74f5f5","resolution":{"observed_at":"2026-08-04T08:26:57.415214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-04T08:26:57.474123Z","title":"Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators.arXiv preprint arXiv:2202.11214, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:57.474123Z"},"links":{"cited_paper":"/paper/2202.11214","citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:cd5b953300b1ac7828de0a76c8ec11e5da74e0eb3c5b57b3baed0e9fdeb94fe0","observation_id":"bd90beab-50f8-457c-934a-da69218b836e","resolution":{"observed_at":"2026-08-04T08:26:57.474123Z","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":"2026-08-04T08:26:57.542705Z","title":"Probabilistic weather forecasting with machine learning.Nature, 637(8044):84–90, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:57.542705Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:0a2118ed0a4c77a69584479c0d50e6e4fa318388f81d24bea515eda08361914a","observation_id":"f17769ee-118b-4d2a-b98c-e5a663bb4602","resolution":{"observed_at":"2026-08-04T08:26:57.542705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.05925","last_updated":"2024-08-09T05:14:05Z","snapshot_observed_at":"2026-08-19T21:45:25.497014Z","submitted_at":"2024-05-09T17:15:09Z","title":"FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.05925","snapshot_observed_at":"2026-08-04T08:26:57.604185Z","title":"Fuxi-ens: A machine learning model for medium-range ensemble weather forecasting.arXiv preprint arXiv:2405.05925, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:57.604185Z"},"links":{"cited_paper":"/paper/2405.05925","citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:b58e54fcf06c6eedfead03cd7b460777e0a86fc71dbf17c641abb194232bde2d","observation_id":"30abab26-d86d-4ef0-bf8b-0078954e4397","resolution":{"observed_at":"2026-08-04T08:26:57.604185Z","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":"2026-08-04T08:26:57.685263Z","title":"The era5 global reanalysis.Quarterly Journal of the Royal Meteorological Society, 146(730):1999–2049, 2020","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:57.685263Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:af8fa1ecab590ddf22db7c4a327b370ab70caab406287cb6e5bb504a2ad5b508","observation_id":"8f6394d4-8763-4967-bfe9-3f14dc49d1ea","resolution":{"observed_at":"2026-08-04T08:26:57.685263Z","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":"2026-08-04T08:26:57.798530Z","title":"Dreary state of precipitation in global models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:57.798530Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:dd79bbce86265139615172ce55eff0ec36c7bd2537d3c3adf8a365d0a1c9b110","observation_id":"bc415437-8f32-419d-a0a7-ac8be3db5c82","resolution":{"observed_at":"2026-08-04T08:26:57.798530Z","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":"2026-08-04T08:26:57.900690Z","title":"The quiet revolution of numerical weather prediction.Nature, 525(7567):47–55, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:57.900690Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:8e4ab3111c68937afd3c2cdaf0b12a85bbeb3838f6c51fed09085f171907902e","observation_id":"47ef305e-ec6f-4e2d-b3f7-70fd4147db82","resolution":{"observed_at":"2026-08-04T08:26:57.900690Z","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":"2026-08-04T08:26:58.004655Z","title":"A review of global precipitation data sets: Data sources, estimation, and intercomparisons.Reviews of geophysics, 56(1):79–107, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:58.004655Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:cd5f0ba7d8afb7a89e95331fa6871c4f6f069c408826d2ece0d1c5d633108d21","observation_id":"abacfc41-ba0f-4027-b2b5-be48138fd08e","resolution":{"observed_at":"2026-08-04T08:26:58.004655Z","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":"2026-08-19T14:35:38.370053Z","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":"2026-08-04T08:26:58.090300Z","title":"Gencast: Diffusion-based ensemble forecasting for medium-range weather.arXiv preprint arXiv:2312.15796, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:58.090300Z"},"links":{"cited_paper":"/paper/2312.15796","citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:d1f20b451e11bacab6d87c64e9f8408b7f3da62201a73e8b129c51b70a584ad9","observation_id":"229f7787-a22a-4ec8-9f6d-b7f638df3c7d","resolution":{"observed_at":"2026-08-04T08:26:58.090300Z","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":"2026-08-04T08:26:58.160428Z","title":"An introduction to multivariate probabilistic forecast evaluation.Energy and AI, 4:100058, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:58.160428Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:0cb615b67310f273637a3dff6bb3dc0d069af756b57f38fd52f640154c758738","observation_id":"faddd300-552d-48e4-b6f4-f9db2094fae8","resolution":{"observed_at":"2026-08-04T08:26:58.160428Z","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":"2026-08-04T08:26:58.303715Z","title":"The era5 global reanalysis.Quarterly journal of the royal meteorological society, 146(730):1999–2049, 2020","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:58.303715Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:a214778d0bd227d9dc85e2822477463ed9ab52b1b63e7b6b49770606b954a346","observation_id":"3d0e8f90-20f9-48d0-a12b-0f8d7ecaced6","resolution":{"observed_at":"2026-08-04T08:26:58.303715Z","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":"2026-08-04T08:26:58.444077Z","title":"Huffman, David T Bolvin, Dan Braithwaite, Kuolin Hsu, Robert Joyce, Christopher Kidd, Eric J Nelkin, Soroosh Sorooshian, Jackson Tan, and Pingping Xie","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:58.444077Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:dbc64b7b088f72f1414b5da0e7f674ab9de8e10234730e365cf6f57704f609a6","observation_id":"ca6b080f-7591-46c4-bea4-9fd0c93055ae","resolution":{"observed_at":"2026-08-04T08:26:58.444077Z","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":"2026-08-04T08:26:58.609920Z","title":"Performance of imerg as a function of spatiotemporal scale.Journal of Hydrometeorology, 18(2):307–319, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:58.609920Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:8576c0906059bca9227e917db0e284e517390574a6238714930048a94711bbf9","observation_id":"6e0a6bd4-27b9-4855-89ef-4ceccae6f5bb","resolution":{"observed_at":"2026-08-04T08:26:58.609920Z","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":"2026-08-04T08:26:58.758444Z","title":"Global forecast system (gfs)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:58.758444Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:1070ea4abd1db09d5eb35feb5ee2b17adce05b35988895c5ea89d63aae1acf05","observation_id":"0526b1fc-4227-424a-95dd-6ee7afeae026","resolution":{"observed_at":"2026-08-04T08:26:58.758444Z","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":"2026-08-04T08:26:58.823365Z","title":"Global ensemble forecast system (gefs)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:58.823365Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:5bd0e2cb9a3eede7ae4b96fa0c99d51ef9ec7902bc006e2c12272e3e48ec298f","observation_id":"bdd13732-4f78-494b-af34-bd42c9d48807","resolution":{"observed_at":"2026-08-04T08:26:58.823365Z","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":"2026-08-04T08:26:58.959418Z","title":"Fuxi: a cascade machine learning forecasting system for 15-day global weather forecast.npj climate and atmospheric science, 6(1):190, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:58.959418Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:3a2b4fc735207a17a5e6f33bbf1f00b98e64dbeab823e45c6c098b523bcb1fd0","observation_id":"4188dfea-b2a0-4627-a8bd-e5031f674ea7","resolution":{"observed_at":"2026-08-04T08:26:58.959418Z","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":"2026-08-04T08:26:59.067337Z","title":"Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.Advances in neural information processing systems, 35:10078–10093, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:59.067337Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:efe547c5ac46815f357fe4533f94418e75d8a6635de4acae0cdb683d3abe2a5a","observation_id":"07e1fc77-9fb5-4e29-b948-273847966cdf","resolution":{"observed_at":"2026-08-04T08:26:59.067337Z","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":"2026-08-04T08:26:59.226208Z","title":"Film: Visual reasoning with a general conditioning layer","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:59.226208Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:fe3e62b05955a3875b1e274476bc1fbc4120cb8bde5e2a89cd753d8deab5a522","observation_id":"1ff286fb-0df6-4972-bc9f-7523ced68197","resolution":{"observed_at":"2026-08-04T08:26:59.226208Z","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":"2026-08-04T08:26:59.383566Z","title":"Gnn-film: Graph neural networks with feature-wise linear modulation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:59.383566Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:49134b9d98de0b004b423b5202765aa3d19e5e4c6cbe4639cc4f6f720026a102","observation_id":"22da3d8f-fcbe-48a2-bebc-3e456bcc7526","resolution":{"observed_at":"2026-08-04T08:26:59.383566Z","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":"2026-08-04T08:26:59.608969Z","title":"Learning skillful medium-range global weather forecasting.Science, 382(6677):1416–1421, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:59.608969Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:29efc86f635fce488492050b3358f1ab19db039a8a475326966bfa4fb1fddc44","observation_id":"b6279614-64c7-4c9a-be23-774cf1f3a5f1","resolution":{"observed_at":"2026-08-04T08:26:59.608969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.11277","last_updated":"2023-09-12T16:28:00Z","snapshot_observed_at":"2026-08-01T19:01:47.393546Z","submitted_at":"2023-04-21T23:52:27Z","title":"PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.11277","snapshot_observed_at":"2026-08-04T08:26:59.755095Z","title":"Pytorch fsdp: experiences on scaling fully sharded data parallel.arXiv preprint arXiv:2304.11277, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:59.755095Z"},"links":{"cited_paper":"/paper/2304.11277","citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:ad066218453109f9440eba4e566b17851061ddfa5a5ec33bd6386bc97d26e1eb","observation_id":"265200c5-91dc-4e14-860d-2ff68633e242","resolution":{"observed_at":"2026-08-04T08:26:59.755095Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-04T08:26:59.915573Z","title":"The blue horizontal line denotes the GEFS baseline (0), while the red curves represent the relative BS of the CSU-PCAST model compared with GEFS","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-04T08:26:59.915573Z"},"links":{"citing_paper":"/paper/2510.20769"},"observation_digest":"sha256:6e1d5c8341c34b376dbff09e02a6adeb6eff7a9667c5b53635fc7a60ad5836fa","observation_id":"e4838ee2-4bf2-4f27-8a83-332962895dc5","resolution":{"observed_at":"2026-08-04T08:26:59.915573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2510.20769","last_updated":"2026-06-24T20:52:29Z","latest_version":2,"primary_category":"physics.ao-ph","snapshot_observed_at":"2026-08-18T18:11:18.379236Z","submitted_at":"2025-10-23T17:43:38Z","title":"CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":25},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2510.20769."}