{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZACEAP6W7LS5I4ZTDUL2MMBEJ3","short_pith_number":"pith:ZACEAP6W","schema_version":"1.0","canonical_sha256":"c804403fd6fae5d473331d17a630244efddc1963c644cbaf0b1acb604eb42cbe","source":{"kind":"arxiv","id":"2411.05420","version":2},"attestation_state":"computed","paper":{"title":"WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","physics.ao-ph"],"primary_cat":"cs.LG","authors_text":"Ben Fei, Hao Chen, Junchao Gong, Lei Bai, Shiqi Chen, Wanli Ouyang, Wenlong Zhang, Xiangyu Chen, Xiangyu Zhao, Xiao-Ming Wu, Yihao Liu, Zhiwang Zhou","submitted_at":"2024-11-08T09:14:19Z","abstract_excerpt":"The Earth's weather system encompasses intricate weather data modalities and diverse weather understanding tasks, which hold significant value to human life. Existing data-driven models focus on single weather understanding tasks (e.g., weather forecasting). Although these models have achieved promising results, they fail to tackle various complex tasks within a single and unified model. Moreover, the paradigm that relies on limited real observations for a single scenario hinders the model's performance upper bound. In response to these limitations, we draw inspiration from the in-context lear"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.05420","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-08T09:14:19Z","cross_cats_sorted":["cs.AI","cs.CV","physics.ao-ph"],"title_canon_sha256":"20a883349d92b88e4786286806a6ee675d5043744d7ce29597629bbc1ed7ad9b","abstract_canon_sha256":"0d84733190c55f189279f2ca5f557b0d5046b67aa96c9096e697e791839b755f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:57.457083Z","signature_b64":"evck6HRCo+VQ4j/eYf5s5wxfyDGYIFwnUHeVHerkYtRd8C4bu9nT3qfJ36s34kw9qrBLccpebJbHrzYM3T4aBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c804403fd6fae5d473331d17a630244efddc1963c644cbaf0b1acb604eb42cbe","last_reissued_at":"2026-07-05T09:45:57.456569Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:57.456569Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","physics.ao-ph"],"primary_cat":"cs.LG","authors_text":"Ben Fei, Hao Chen, Junchao Gong, Lei Bai, Shiqi Chen, Wanli Ouyang, Wenlong Zhang, Xiangyu Chen, Xiangyu Zhao, Xiao-Ming Wu, Yihao Liu, Zhiwang Zhou","submitted_at":"2024-11-08T09:14:19Z","abstract_excerpt":"The Earth's weather system encompasses intricate weather data modalities and diverse weather understanding tasks, which hold significant value to human life. Existing data-driven models focus on single weather understanding tasks (e.g., weather forecasting). Although these models have achieved promising results, they fail to tackle various complex tasks within a single and unified model. Moreover, the paradigm that relies on limited real observations for a single scenario hinders the model's performance upper bound. In response to these limitations, we draw inspiration from the in-context lear"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.05420","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.05420/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.05420","created_at":"2026-07-05T09:45:57.456633+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.05420v2","created_at":"2026-07-05T09:45:57.456633+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.05420","created_at":"2026-07-05T09:45:57.456633+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZACEAP6W7LS5","created_at":"2026-07-05T09:45:57.456633+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZACEAP6W7LS5I4ZT","created_at":"2026-07-05T09:45:57.456633+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZACEAP6W","created_at":"2026-07-05T09:45:57.456633+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09486","citing_title":"LangRetrieval: Language-Guided Self-Evolving Satellite-to-Radar Retrieval via CSI-Driven Reward","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12542","citing_title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12542","citing_title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","ref_index":44,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZACEAP6W7LS5I4ZTDUL2MMBEJ3","json":"https://pith.science/pith/ZACEAP6W7LS5I4ZTDUL2MMBEJ3.json","graph_json":"https://pith.science/api/pith-number/ZACEAP6W7LS5I4ZTDUL2MMBEJ3/graph.json","events_json":"https://pith.science/api/pith-number/ZACEAP6W7LS5I4ZTDUL2MMBEJ3/events.json","paper":"https://pith.science/paper/ZACEAP6W"},"agent_actions":{"view_html":"https://pith.science/pith/ZACEAP6W7LS5I4ZTDUL2MMBEJ3","download_json":"https://pith.science/pith/ZACEAP6W7LS5I4ZTDUL2MMBEJ3.json","view_paper":"https://pith.science/paper/ZACEAP6W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.05420&json=true","fetch_graph":"https://pith.science/api/pith-number/ZACEAP6W7LS5I4ZTDUL2MMBEJ3/graph.json","fetch_events":"https://pith.science/api/pith-number/ZACEAP6W7LS5I4ZTDUL2MMBEJ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZACEAP6W7LS5I4ZTDUL2MMBEJ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZACEAP6W7LS5I4ZTDUL2MMBEJ3/action/storage_attestation","attest_author":"https://pith.science/pith/ZACEAP6W7LS5I4ZTDUL2MMBEJ3/action/author_attestation","sign_citation":"https://pith.science/pith/ZACEAP6W7LS5I4ZTDUL2MMBEJ3/action/citation_signature","submit_replication":"https://pith.science/pith/ZACEAP6W7LS5I4ZTDUL2MMBEJ3/action/replication_record"}},"created_at":"2026-07-05T09:45:57.456633+00:00","updated_at":"2026-07-05T09:45:57.456633+00:00"}