{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ONFNJHF43JRIXY2JJNLB6MRUGL","short_pith_number":"pith:ONFNJHF4","canonical_record":{"source":{"id":"2605.06944","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-07T21:04:05Z","cross_cats_sorted":[],"title_canon_sha256":"8cef03696c862f2a1a07072dcdc314b9c13597e2e8353a3b30c693809ef77337","abstract_canon_sha256":"89464a08ba745777660d7909494ca52fc657c7888c7071749d3924a5877c0774"},"schema_version":"1.0"},"canonical_sha256":"734ad49cbcda628be3494b561f323432e1f61525237b79b44272a5baf3ccc41d","source":{"kind":"arxiv","id":"2605.06944","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.06944","created_at":"2026-05-20T02:05:44Z"},{"alias_kind":"arxiv_version","alias_value":"2605.06944v2","created_at":"2026-05-20T02:05:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.06944","created_at":"2026-05-20T02:05:44Z"},{"alias_kind":"pith_short_12","alias_value":"ONFNJHF43JRI","created_at":"2026-05-20T02:05:44Z"},{"alias_kind":"pith_short_16","alias_value":"ONFNJHF43JRIXY2J","created_at":"2026-05-20T02:05:44Z"},{"alias_kind":"pith_short_8","alias_value":"ONFNJHF4","created_at":"2026-05-20T02:05:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ONFNJHF43JRIXY2JJNLB6MRUGL","target":"record","payload":{"canonical_record":{"source":{"id":"2605.06944","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-07T21:04:05Z","cross_cats_sorted":[],"title_canon_sha256":"8cef03696c862f2a1a07072dcdc314b9c13597e2e8353a3b30c693809ef77337","abstract_canon_sha256":"89464a08ba745777660d7909494ca52fc657c7888c7071749d3924a5877c0774"},"schema_version":"1.0"},"canonical_sha256":"734ad49cbcda628be3494b561f323432e1f61525237b79b44272a5baf3ccc41d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T02:05:44.688251Z","signature_b64":"9tMDIQH72S9vVf2vc0nn+splN+vI6OMdGMgIu6rcXWOr7DxF4Wm6Z7K0y/3jF8Fi2Q30vtp1mP7k8lzMsr4rAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"734ad49cbcda628be3494b561f323432e1f61525237b79b44272a5baf3ccc41d","last_reissued_at":"2026-05-20T02:05:44.687216Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T02:05:44.687216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.06944","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T02:05:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EgAIOz5cfFgsx3hJGX/SQvZSWpXvczhsG8bTVrQMNSD1toag7b2V47vw8j+sNxE/tb5nQ/eWt4To1n+/KagSDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T12:33:58.320486Z"},"content_sha256":"a502cded98e1e9aaab18fea2d97d9b5893001ee99342a33fee5430fa0ed484ba","schema_version":"1.0","event_id":"sha256:a502cded98e1e9aaab18fea2d97d9b5893001ee99342a33fee5430fa0ed484ba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ONFNJHF43JRIXY2JJNLB6MRUGL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AIMIP Phase 1: systematic evaluations of AI weather and climate models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"AI weather and climate models simulate historical climate and forcing responses as well as conventional physically-based models, though some underestimate warming trends and diverge in out-of-sample tests.","cross_cats":[],"primary_cat":"physics.ao-ph","authors_text":"Antonia Jost, Brian Henn, Christian Lessig, Christopher S. Bretherton, Dale Durran, Dmitrii Kochkov, Guillaume Couairon, Ignacio Lopez-Gomez, Janni Yuval, Kyle Joseph Chen Hall, Maria J. Molina, Nathaniel Cresswell-Clay, Nikolay Koldunov, Noah Brenowitz, Oliver Watt-Meyer, Peter Manshausen, Renu Singh, Robert Brunstein, Stephan Hoyer, Troy Arcomano, Yana Hasson","submitted_at":"2026-05-07T21:04:05Z","abstract_excerpt":"We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a common experiment, output data format, and training constraints (namely, training against historical reanalysis data) for AIMIP Phase 1 models. We aim to identify differences in modeling frameworks and AI architectural choices that influence model behavior, and build trust in AI weather and climate models through open data and evaluation. AIMIP Phase 1 models must simulate the atmosphere given specified historical s"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We find that the AI models are able to simulate the historical climate and response to forcing as well as a conventional physically-based model, but some AI models underestimate historical warming trends, and their predictions diverge in the out-of-sample generalization tests.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That training solely against historical reanalysis data under the stated constraints, combined with the five chosen evaluation criteria, is sufficient to assess and build trust in the models' reliability for climate applications.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"AIMIP Phase 1 shows AI models simulate historical climate and El Niño responses as well as traditional models, though some underestimate trends and diverge in generalization tests, with a public dataset released for further checks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"AI weather and climate models simulate historical climate and forcing responses as well as conventional physically-based models, though some underestimate warming trends and diverge in out-of-sample tests.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"dca7f336c8dff5f22114c24fc3e7be87930f6725301061e5f44051537aedb5db"},"source":{"id":"2605.06944","kind":"arxiv","version":2},"verdict":{"id":"40865847-c3ef-42f0-8da8-23a947398ad9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T00:57:30.221122Z","strongest_claim":"We find that the AI models are able to simulate the historical climate and response to forcing as well as a conventional physically-based model, but some AI models underestimate historical warming trends, and their predictions diverge in the out-of-sample generalization tests.","one_line_summary":"AIMIP Phase 1 shows AI models simulate historical climate and El Niño responses as well as traditional models, though some underestimate trends and diverge in generalization tests, with a public dataset released for further checks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That training solely against historical reanalysis data under the stated constraints, combined with the five chosen evaluation criteria, is sufficient to assess and build trust in the models' reliability for climate applications.","pith_extraction_headline":"AI weather and climate models simulate historical climate and forcing responses as well as conventional physically-based models, though some underestimate warming trends and diverge in out-of-sample tests."},"integrity":{"clean":false,"summary":{"advisory":1,"critical":0,"by_detector":{"doi_compliance":{"total":1,"advisory":1,"critical":0,"informational":0}},"informational":0},"endpoint":"/pith/2605.06944/integrity.json","findings":[{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1175/1520-0477(1999) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":12,"audited_at":"2026-05-19T12:13:49.457652Z","detected_doi":"10.1175/1520-0477(1999","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null}],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T17:31:19.280289Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T12:13:49.457652Z","status":"completed","version":"1.0.0","findings_count":1}],"snapshot_sha256":"430dfb6477d0686921546b365193cbe03e0f3bac69ba255d9351363554f683f6"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":1,"snapshot_sha256":"4cf4f89be03a478deceb9351de550fae88d35e75c02730c5cd053c9253a63491"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"40865847-c3ef-42f0-8da8-23a947398ad9"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T02:05:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RLHd7fM6b30p2qOevWZzgtAyJMEsHXyEFNlh0g/Wa5L1rHAhx7gEF5j7XYaFM8dTQAwkT5vBV+hOEBc1VsG5Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T12:33:58.321233Z"},"content_sha256":"9d90500c62015f01c89001cb2bde1036d3327e72b43179753c66f1549a7c9581","schema_version":"1.0","event_id":"sha256:9d90500c62015f01c89001cb2bde1036d3327e72b43179753c66f1549a7c9581"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:ONFNJHF43JRIXY2JJNLB6MRUGL","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1175/1520-0477(1999) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Gates, W. L., Boyle, J. S., Covey, C., Dease, C. G., Doutriaux, C. M., Drach, R. S., Fiorino, M., Gleckler, P. J., Hnilo, J. J., Marlais, S. M., Phillips, T. J., Potter, G. L., Santer, B. D., Sperber, K. R., Taylor, K. E., and Williams, D. ","arxiv_id":"2605.06944","detector":"doi_compliance","evidence":{"ref_index":12,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"Gates, W. L., Boyle, J. S., Covey, C., Dease, C. G., Doutriaux, C. M., Drach, R. S., Fiorino, M., Gleckler, P. J., Hnilo, J. J., Marlais, S. M., Phillips, T. J., Potter, G. L., Santer, B. D., Sperber, K. R., Taylor, K. E., and Williams, D. ","reconstructed_doi":"10.1175/1520-0477(1999"},"severity":"advisory","ref_index":12,"audited_at":"2026-05-19T12:13:49.457652Z","event_type":"pith.integrity.v1","detected_doi":"10.1175/1520-0477(1999","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"f8b185e057506eedb1d0c1210bcbeb286bc4e07933bcd8c393f0273304ce77fd","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":1183,"payload_sha256":"9e0a82ed180b5d57cac84dbcdcdc55887370b80fa638d2c3285ff051757ed435","signature_b64":"ur9++AFXY7bo942GTu9OWOtgsxjskjEd3FBymRo2uEXZC+teRr2eLLRYL4kGQIDZtCr0od9pzyM/vds8pUm7Bg==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-19T12:17:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LRwY9uAz0FWxfULbfrLvLNso5SR/ICZY/V1BLPuaDuZ46bbrCWqd5hzoxbUKLxKGIUlcUbpUM49l0OAFMdBSBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T12:33:58.323696Z"},"content_sha256":"5dbe1a05b9a645541760e56ae77f0ae69de2be4abe21bf98018ae1ee5504e8b8","schema_version":"1.0","event_id":"sha256:5dbe1a05b9a645541760e56ae77f0ae69de2be4abe21bf98018ae1ee5504e8b8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ONFNJHF43JRIXY2JJNLB6MRUGL/bundle.json","state_url":"https://pith.science/pith/ONFNJHF43JRIXY2JJNLB6MRUGL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ONFNJHF43JRIXY2JJNLB6MRUGL/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":"2026-05-21T12:33:58Z","links":{"resolver":"https://pith.science/pith/ONFNJHF43JRIXY2JJNLB6MRUGL","bundle":"https://pith.science/pith/ONFNJHF43JRIXY2JJNLB6MRUGL/bundle.json","state":"https://pith.science/pith/ONFNJHF43JRIXY2JJNLB6MRUGL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ONFNJHF43JRIXY2JJNLB6MRUGL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ONFNJHF43JRIXY2JJNLB6MRUGL","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"89464a08ba745777660d7909494ca52fc657c7888c7071749d3924a5877c0774","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-07T21:04:05Z","title_canon_sha256":"8cef03696c862f2a1a07072dcdc314b9c13597e2e8353a3b30c693809ef77337"},"schema_version":"1.0","source":{"id":"2605.06944","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.06944","created_at":"2026-05-20T02:05:44Z"},{"alias_kind":"arxiv_version","alias_value":"2605.06944v2","created_at":"2026-05-20T02:05:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.06944","created_at":"2026-05-20T02:05:44Z"},{"alias_kind":"pith_short_12","alias_value":"ONFNJHF43JRI","created_at":"2026-05-20T02:05:44Z"},{"alias_kind":"pith_short_16","alias_value":"ONFNJHF43JRIXY2J","created_at":"2026-05-20T02:05:44Z"},{"alias_kind":"pith_short_8","alias_value":"ONFNJHF4","created_at":"2026-05-20T02:05:44Z"}],"graph_snapshots":[{"event_id":"sha256:9d90500c62015f01c89001cb2bde1036d3327e72b43179753c66f1549a7c9581","target":"graph","created_at":"2026-05-20T02:05:44Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We find that the AI models are able to simulate the historical climate and response to forcing as well as a conventional physically-based model, but some AI models underestimate historical warming trends, and their predictions diverge in the out-of-sample generalization tests."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That training solely against historical reanalysis data under the stated constraints, combined with the five chosen evaluation criteria, is sufficient to assess and build trust in the models' reliability for climate applications."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"AIMIP Phase 1 shows AI models simulate historical climate and El Niño responses as well as traditional models, though some underestimate trends and diverge in generalization tests, with a public dataset released for further checks."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"AI weather and climate models simulate historical climate and forcing responses as well as conventional physically-based models, though some underestimate warming trends and diverge in out-of-sample tests."}],"snapshot_sha256":"dca7f336c8dff5f22114c24fc3e7be87930f6725301061e5f44051537aedb5db"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"4cf4f89be03a478deceb9351de550fae88d35e75c02730c5cd053c9253a63491"},"integrity":{"available":true,"clean":false,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T17:31:19.280289Z","status":"completed","version":"1.0.0"},{"findings_count":1,"name":"doi_compliance","ran_at":"2026-05-19T12:13:49.457652Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.06944/integrity.json","findings":[{"audited_at":"2026-05-19T12:13:49.457652Z","detected_arxiv_id":null,"detected_doi":"10.1175/1520-0477(1999","detector":"doi_compliance","finding_type":"recoverable_identifier","note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1175/1520-0477(1999) was visible in the surrounding text but could not be confirmed against doi.org as printed.","ref_index":12,"severity":"advisory","verdict_class":"incontrovertible"}],"snapshot_sha256":"430dfb6477d0686921546b365193cbe03e0f3bac69ba255d9351363554f683f6","summary":{"advisory":1,"by_detector":{"doi_compliance":{"advisory":1,"critical":0,"informational":0,"total":1}},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present the AI weather and climate model intercomparison project (AIMIP), phase 1. Drawing from the rich tradition of intercomparisons in climate model development, we specify a common experiment, output data format, and training constraints (namely, training against historical reanalysis data) for AIMIP Phase 1 models. We aim to identify differences in modeling frameworks and AI architectural choices that influence model behavior, and build trust in AI weather and climate models through open data and evaluation. AIMIP Phase 1 models must simulate the atmosphere given specified historical s","authors_text":"Antonia Jost, Brian Henn, Christian Lessig, Christopher S. Bretherton, Dale Durran, Dmitrii Kochkov, Guillaume Couairon, Ignacio Lopez-Gomez, Janni Yuval, Kyle Joseph Chen Hall, Maria J. Molina, Nathaniel Cresswell-Clay, Nikolay Koldunov, Noah Brenowitz, Oliver Watt-Meyer, Peter Manshausen, Renu Singh, Robert Brunstein, Stephan Hoyer, Troy Arcomano, Yana Hasson","cross_cats":[],"headline":"AI weather and climate models simulate historical climate and forcing responses as well as conventional physically-based models, though some underestimate warming trends and diverge in out-of-sample tests.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-07T21:04:05Z","title":"AIMIP Phase 1: systematic evaluations of AI weather and climate models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.06944","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-11T00:57:30.221122Z","id":"40865847-c3ef-42f0-8da8-23a947398ad9","model_set":{"reader":"grok-4.3"},"one_line_summary":"AIMIP Phase 1 shows AI models simulate historical climate and El Niño responses as well as traditional models, though some underestimate trends and diverge in generalization tests, with a public dataset released for further checks.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"AI weather and climate models simulate historical climate and forcing responses as well as conventional physically-based models, though some underestimate warming trends and diverge in out-of-sample tests.","strongest_claim":"We find that the AI models are able to simulate the historical climate and response to forcing as well as a conventional physically-based model, but some AI models underestimate historical warming trends, and their predictions diverge in the out-of-sample generalization tests.","weakest_assumption":"That training solely against historical reanalysis data under the stated constraints, combined with the five chosen evaluation criteria, is sufficient to assess and build trust in the models' reliability for climate applications."}},"verdict_id":"40865847-c3ef-42f0-8da8-23a947398ad9"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a502cded98e1e9aaab18fea2d97d9b5893001ee99342a33fee5430fa0ed484ba","target":"record","created_at":"2026-05-20T02:05:44Z","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":"89464a08ba745777660d7909494ca52fc657c7888c7071749d3924a5877c0774","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-07T21:04:05Z","title_canon_sha256":"8cef03696c862f2a1a07072dcdc314b9c13597e2e8353a3b30c693809ef77337"},"schema_version":"1.0","source":{"id":"2605.06944","kind":"arxiv","version":2}},"canonical_sha256":"734ad49cbcda628be3494b561f323432e1f61525237b79b44272a5baf3ccc41d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"734ad49cbcda628be3494b561f323432e1f61525237b79b44272a5baf3ccc41d","first_computed_at":"2026-05-20T02:05:44.687216Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T02:05:44.687216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9tMDIQH72S9vVf2vc0nn+splN+vI6OMdGMgIu6rcXWOr7DxF4Wm6Z7K0y/3jF8Fi2Q30vtp1mP7k8lzMsr4rAw==","signature_status":"signed_v1","signed_at":"2026-05-20T02:05:44.688251Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.06944","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5dbe1a05b9a645541760e56ae77f0ae69de2be4abe21bf98018ae1ee5504e8b8","sha256:a502cded98e1e9aaab18fea2d97d9b5893001ee99342a33fee5430fa0ed484ba","sha256:9d90500c62015f01c89001cb2bde1036d3327e72b43179753c66f1549a7c9581"],"state_sha256":"faa23c4e3aeef4fb2539996ad1fd2e7087dfa51256da2369c8f23405d3f8c45c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T2sraPAt+VHrSYJwCaHeIcJdQbG/P7lCEfi4bkTPGiftAd9atU/vz+o+9S7SsQ5ZO8POWOrLluqhBnNOSvhcCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T12:33:58.327152Z","bundle_sha256":"55d2382969a37dca59977684aff7991f1bb689e304d3e1a6e99a02f41a11e2b9"}}