{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:HDAATTHK6POIWLA3CPAW5PCSXA","short_pith_number":"pith:HDAATTHK","canonical_record":{"source":{"id":"2410.07472","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-09T22:25:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"46f7d643ad062be1afeebea0a78eb48375c6dc89d23ea70b0cba352c6ccc3071","abstract_canon_sha256":"c9f4016fc17f160c95976682f7a2bcd9905666f55b00486ce49d86ea0c823036"},"schema_version":"1.0"},"canonical_sha256":"38c009cceaf3dc8b2c1b13c16ebc52b8033f4e27f9ea1a4adabb4fc145f51b0f","source":{"kind":"arxiv","id":"2410.07472","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.07472","created_at":"2026-07-05T09:18:37Z"},{"alias_kind":"arxiv_version","alias_value":"2410.07472v1","created_at":"2026-07-05T09:18:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.07472","created_at":"2026-07-05T09:18:37Z"},{"alias_kind":"pith_short_12","alias_value":"HDAATTHK6POI","created_at":"2026-07-05T09:18:37Z"},{"alias_kind":"pith_short_16","alias_value":"HDAATTHK6POIWLA3","created_at":"2026-07-05T09:18:37Z"},{"alias_kind":"pith_short_8","alias_value":"HDAATTHK","created_at":"2026-07-05T09:18:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:HDAATTHK6POIWLA3CPAW5PCSXA","target":"record","payload":{"canonical_record":{"source":{"id":"2410.07472","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-09T22:25:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"46f7d643ad062be1afeebea0a78eb48375c6dc89d23ea70b0cba352c6ccc3071","abstract_canon_sha256":"c9f4016fc17f160c95976682f7a2bcd9905666f55b00486ce49d86ea0c823036"},"schema_version":"1.0"},"canonical_sha256":"38c009cceaf3dc8b2c1b13c16ebc52b8033f4e27f9ea1a4adabb4fc145f51b0f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:18:37.753678Z","signature_b64":"1avJfQX7wdbMb/d7Cr/T6rXbOoB/VdPL7YrAWaTP6ed5tu1EAfNJxVJMHAG0yegsDDJLl1jONtBK1x2LgRUxCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38c009cceaf3dc8b2c1b13c16ebc52b8033f4e27f9ea1a4adabb4fc145f51b0f","last_reissued_at":"2026-07-05T09:18:37.753246Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:18:37.753246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.07472","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:18:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6xd7Md1EqZh/AluNvrmCt0uZwcI1NLcouaXi21LY9/aEDhC/NGIkLyqjQPjdnKbiuEhd8hZqN/0rJ5kNHzhFAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T08:32:48.398119Z"},"content_sha256":"cb8d5d3e014f229a5e10d3c0c3f809070d3acf93c0c82f270c5a775c5896f22a","schema_version":"1.0","event_id":"sha256:cb8d5d3e014f229a5e10d3c0c3f809070d3acf93c0c82f270c5a775c5896f22a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:HDAATTHK6POIWLA3CPAW5PCSXA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Exploring the design space of deep-learning-based weather forecasting systems","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Boris Bonev, Christopher Choy, David Krueger, Jan Kautz, Jean Kossaifi, Kamyar Azizzadenesheli, Shoaib Ahmed Siddiqui","submitted_at":"2024-10-09T22:25:50Z","abstract_excerpt":"Despite tremendous progress in developing deep-learning-based weather forecasting systems, their design space, including the impact of different design choices, is yet to be well understood. This paper aims to fill this knowledge gap by systematically analyzing these choices including architecture, problem formulation, pretraining scheme, use of image-based pretrained models, loss functions, noise injection, multi-step inputs, additional static masks, multi-step finetuning (including larger stride models), as well as training on a larger dataset. We study fixed-grid architectures such as UNet,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.07472","kind":"arxiv","version":1},"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/2410.07472/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:18:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H9Nni/T8D95NVFmslO5bneBAi4dIhIZaSyZtODjKTqQIzM0ou4nIBGfHm6hj7B9eVCEGc2h3sYktAl/MO5ycCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T08:32:48.398644Z"},"content_sha256":"ab195559c27728c7fd6465cb7b5f4d918534b8ec37dfba698b91965af52e67fb","schema_version":"1.0","event_id":"sha256:ab195559c27728c7fd6465cb7b5f4d918534b8ec37dfba698b91965af52e67fb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HDAATTHK6POIWLA3CPAW5PCSXA/bundle.json","state_url":"https://pith.science/pith/HDAATTHK6POIWLA3CPAW5PCSXA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HDAATTHK6POIWLA3CPAW5PCSXA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T08:32:48Z","links":{"resolver":"https://pith.science/pith/HDAATTHK6POIWLA3CPAW5PCSXA","bundle":"https://pith.science/pith/HDAATTHK6POIWLA3CPAW5PCSXA/bundle.json","state":"https://pith.science/pith/HDAATTHK6POIWLA3CPAW5PCSXA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HDAATTHK6POIWLA3CPAW5PCSXA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:HDAATTHK6POIWLA3CPAW5PCSXA","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":"c9f4016fc17f160c95976682f7a2bcd9905666f55b00486ce49d86ea0c823036","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-09T22:25:50Z","title_canon_sha256":"46f7d643ad062be1afeebea0a78eb48375c6dc89d23ea70b0cba352c6ccc3071"},"schema_version":"1.0","source":{"id":"2410.07472","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.07472","created_at":"2026-07-05T09:18:37Z"},{"alias_kind":"arxiv_version","alias_value":"2410.07472v1","created_at":"2026-07-05T09:18:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.07472","created_at":"2026-07-05T09:18:37Z"},{"alias_kind":"pith_short_12","alias_value":"HDAATTHK6POI","created_at":"2026-07-05T09:18:37Z"},{"alias_kind":"pith_short_16","alias_value":"HDAATTHK6POIWLA3","created_at":"2026-07-05T09:18:37Z"},{"alias_kind":"pith_short_8","alias_value":"HDAATTHK","created_at":"2026-07-05T09:18:37Z"}],"graph_snapshots":[{"event_id":"sha256:ab195559c27728c7fd6465cb7b5f4d918534b8ec37dfba698b91965af52e67fb","target":"graph","created_at":"2026-07-05T09:18:37Z","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/2410.07472/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite tremendous progress in developing deep-learning-based weather forecasting systems, their design space, including the impact of different design choices, is yet to be well understood. This paper aims to fill this knowledge gap by systematically analyzing these choices including architecture, problem formulation, pretraining scheme, use of image-based pretrained models, loss functions, noise injection, multi-step inputs, additional static masks, multi-step finetuning (including larger stride models), as well as training on a larger dataset. We study fixed-grid architectures such as UNet,","authors_text":"Boris Bonev, Christopher Choy, David Krueger, Jan Kautz, Jean Kossaifi, Kamyar Azizzadenesheli, Shoaib Ahmed Siddiqui","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-09T22:25:50Z","title":"Exploring the design space of deep-learning-based weather forecasting systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.07472","kind":"arxiv","version":1},"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:cb8d5d3e014f229a5e10d3c0c3f809070d3acf93c0c82f270c5a775c5896f22a","target":"record","created_at":"2026-07-05T09:18:37Z","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":"c9f4016fc17f160c95976682f7a2bcd9905666f55b00486ce49d86ea0c823036","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-09T22:25:50Z","title_canon_sha256":"46f7d643ad062be1afeebea0a78eb48375c6dc89d23ea70b0cba352c6ccc3071"},"schema_version":"1.0","source":{"id":"2410.07472","kind":"arxiv","version":1}},"canonical_sha256":"38c009cceaf3dc8b2c1b13c16ebc52b8033f4e27f9ea1a4adabb4fc145f51b0f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38c009cceaf3dc8b2c1b13c16ebc52b8033f4e27f9ea1a4adabb4fc145f51b0f","first_computed_at":"2026-07-05T09:18:37.753246Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:18:37.753246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1avJfQX7wdbMb/d7Cr/T6rXbOoB/VdPL7YrAWaTP6ed5tu1EAfNJxVJMHAG0yegsDDJLl1jONtBK1x2LgRUxCw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:18:37.753678Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.07472","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb8d5d3e014f229a5e10d3c0c3f809070d3acf93c0c82f270c5a775c5896f22a","sha256:ab195559c27728c7fd6465cb7b5f4d918534b8ec37dfba698b91965af52e67fb"],"state_sha256":"a07dde64c4902973df1a0888ef8a858816d7837241e522a3a3e7c27e0a0e2d6e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mIl69rts2Thil89ZE3yZ20I8C1JlEjdRD62jB+vfbgBGq6SxUeu8m5AS/IvfYV1YBdyIAlpkPk1Y5vcOi7SeCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T08:32:48.401908Z","bundle_sha256":"e3e1b25d842efd81a699bf6ad6426004d6bfbb031b454a5c609e711c98992966"}}