{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FU4DZUGVJOXSYEGZG5HUU6LRL4","short_pith_number":"pith:FU4DZUGV","schema_version":"1.0","canonical_sha256":"2d383cd0d54baf2c10d9374f4a79715f3457880f3b94ee5fc44d7f9bfb80ad52","source":{"kind":"arxiv","id":"2606.02550","version":1},"attestation_state":"computed","paper":{"title":"Probabilistic storyline attribution using machine learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["physics.ao-ph"],"primary_cat":"stat.AP","authors_text":"Frieder Loer, Maybritt Schillinger, Sebastian Sippel","submitted_at":"2026-06-01T17:49:59Z","abstract_excerpt":"A fundamental goal in climate attribution is to estimate how forced climate change contributes to observed extreme weather events. The storyline attribution method compares an observed weather event, conditional on its atmospheric dynamic state (i.e., atmospheric circulation), in the current, 'factual' climate to an event with very similar circulation conditions in a hypothetical, 'counterfactual' climate. However, physical climate models cannot directly transfer these storyline counterfactuals across different climate forcing states. Statistical and machine learning techniques may overcome th"},"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":"2606.02550","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.AP","submitted_at":"2026-06-01T17:49:59Z","cross_cats_sorted":["physics.ao-ph"],"title_canon_sha256":"b87056a869572e506e01923d35a5ad75f94d2b28a3926c10a896c1032c18ebc7","abstract_canon_sha256":"5ce5eb03bff3d9200e6f7dbb99326a91927e8fba9e36e0632440bb230b1b76e1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T03:05:09.368728Z","signature_b64":"sJEOWxLp+o6MEaYsWwD20A/OLfScqeDCDsbgGfPPcoe2guO8LWCIhA/Hc7XqBdgmz7mUSgggvJscgwjLScOmBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d383cd0d54baf2c10d9374f4a79715f3457880f3b94ee5fc44d7f9bfb80ad52","last_reissued_at":"2026-06-02T03:05:09.368344Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T03:05:09.368344Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic storyline attribution using machine learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["physics.ao-ph"],"primary_cat":"stat.AP","authors_text":"Frieder Loer, Maybritt Schillinger, Sebastian Sippel","submitted_at":"2026-06-01T17:49:59Z","abstract_excerpt":"A fundamental goal in climate attribution is to estimate how forced climate change contributes to observed extreme weather events. The storyline attribution method compares an observed weather event, conditional on its atmospheric dynamic state (i.e., atmospheric circulation), in the current, 'factual' climate to an event with very similar circulation conditions in a hypothetical, 'counterfactual' climate. However, physical climate models cannot directly transfer these storyline counterfactuals across different climate forcing states. Statistical and machine learning techniques may overcome th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.02550","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/2606.02550/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":"2606.02550","created_at":"2026-06-02T03:05:09.368400+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.02550v1","created_at":"2026-06-02T03:05:09.368400+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.02550","created_at":"2026-06-02T03:05:09.368400+00:00"},{"alias_kind":"pith_short_12","alias_value":"FU4DZUGVJOXS","created_at":"2026-06-02T03:05:09.368400+00:00"},{"alias_kind":"pith_short_16","alias_value":"FU4DZUGVJOXSYEGZ","created_at":"2026-06-02T03:05:09.368400+00:00"},{"alias_kind":"pith_short_8","alias_value":"FU4DZUGV","created_at":"2026-06-02T03:05:09.368400+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FU4DZUGVJOXSYEGZG5HUU6LRL4","json":"https://pith.science/pith/FU4DZUGVJOXSYEGZG5HUU6LRL4.json","graph_json":"https://pith.science/api/pith-number/FU4DZUGVJOXSYEGZG5HUU6LRL4/graph.json","events_json":"https://pith.science/api/pith-number/FU4DZUGVJOXSYEGZG5HUU6LRL4/events.json","paper":"https://pith.science/paper/FU4DZUGV"},"agent_actions":{"view_html":"https://pith.science/pith/FU4DZUGVJOXSYEGZG5HUU6LRL4","download_json":"https://pith.science/pith/FU4DZUGVJOXSYEGZG5HUU6LRL4.json","view_paper":"https://pith.science/paper/FU4DZUGV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.02550&json=true","fetch_graph":"https://pith.science/api/pith-number/FU4DZUGVJOXSYEGZG5HUU6LRL4/graph.json","fetch_events":"https://pith.science/api/pith-number/FU4DZUGVJOXSYEGZG5HUU6LRL4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FU4DZUGVJOXSYEGZG5HUU6LRL4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FU4DZUGVJOXSYEGZG5HUU6LRL4/action/storage_attestation","attest_author":"https://pith.science/pith/FU4DZUGVJOXSYEGZG5HUU6LRL4/action/author_attestation","sign_citation":"https://pith.science/pith/FU4DZUGVJOXSYEGZG5HUU6LRL4/action/citation_signature","submit_replication":"https://pith.science/pith/FU4DZUGVJOXSYEGZG5HUU6LRL4/action/replication_record"}},"created_at":"2026-06-02T03:05:09.368400+00:00","updated_at":"2026-06-02T03:05:09.368400+00:00"}