{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:IFBCSLYAAMQIF7OL5X7SNIYYKI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f054742e9d331cf17129ffe14cca1d93fda15804252e96f6aed8cfddaa7437ff","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-24T23:19:01Z","title_canon_sha256":"c338605ba409e3cb77f29a25b528e61358f20c69274555548d0182eba0f6d2fb"},"schema_version":"1.0","source":{"id":"2301.10343","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.10343","created_at":"2026-07-05T07:25:22Z"},{"alias_kind":"arxiv_version","alias_value":"2301.10343v5","created_at":"2026-07-05T07:25:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.10343","created_at":"2026-07-05T07:25:22Z"},{"alias_kind":"pith_short_12","alias_value":"IFBCSLYAAMQI","created_at":"2026-07-05T07:25:22Z"},{"alias_kind":"pith_short_16","alias_value":"IFBCSLYAAMQIF7OL","created_at":"2026-07-05T07:25:22Z"},{"alias_kind":"pith_short_8","alias_value":"IFBCSLYA","created_at":"2026-07-05T07:25:22Z"}],"graph_snapshots":[{"event_id":"sha256:7b2b741f663e6dd3c70236eb009b11f48f9bf04d21fc1fc6ec24932af6a2f939","target":"graph","created_at":"2026-07-05T07:25:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2301.10343/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dynamics and complex interactions between multiple variables, which are challenging to approximate. Additionally, many such numerical models are computationally intensive, especially when modeling the atmospheric phenomenon at a fine-grained spatial and temporal resolution. Recent data-driven approaches based on machine learning instead aim to directly solve a downstream forecasting or projection task by learning a data","authors_text":"Aditya Grover, Ashish Kapoor, Jayesh K. Gupta, Johannes Brandstetter, Tung Nguyen","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-24T23:19:01Z","title":"ClimaX: A foundation model for weather and climate"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.10343","kind":"arxiv","version":5},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:544525b62f8949b8a0538bc5ec458cf1b7b3ac31e0c025eeca4e1014a70fe245","target":"record","created_at":"2026-07-05T07:25:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f054742e9d331cf17129ffe14cca1d93fda15804252e96f6aed8cfddaa7437ff","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-24T23:19:01Z","title_canon_sha256":"c338605ba409e3cb77f29a25b528e61358f20c69274555548d0182eba0f6d2fb"},"schema_version":"1.0","source":{"id":"2301.10343","kind":"arxiv","version":5}},"canonical_sha256":"4142292f00032082fdcbedff26a318523e2e16913f53347df4afa2963ba14722","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4142292f00032082fdcbedff26a318523e2e16913f53347df4afa2963ba14722","first_computed_at":"2026-07-05T07:25:22.003625Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:25:22.003625Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6/iK81Ht4kofEvd0lHnvv7qxiw1Ej2KgPnkInQxjagOArfFzAKjiMPHGFrm2DJwd4Yp0aWUWyCymFhzI1NXOCg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:25:22.004100Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.10343","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:544525b62f8949b8a0538bc5ec458cf1b7b3ac31e0c025eeca4e1014a70fe245","sha256:7b2b741f663e6dd3c70236eb009b11f48f9bf04d21fc1fc6ec24932af6a2f939"],"state_sha256":"5a039429d00657e9baac0bfdd511bb18c6832314179811ab6976151bc9262dfb"}