{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:FZLUCJIKWML3IJJABN2P3CIK7S","short_pith_number":"pith:FZLUCJIK","schema_version":"1.0","canonical_sha256":"2e5741250ab317b425200b74fd890afca62e8de485b03be6895d22a7ce4741da","source":{"kind":"arxiv","id":"1504.01933","version":2},"attestation_state":"computed","paper":{"title":"A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Adam A. Scaife, Alberto Arribas, David B. Stephenson, Philip G. Sansom, Rosie Eade, Stefan Siegert","submitted_at":"2015-04-08T12:20:18Z","abstract_excerpt":"Predictability estimates of ensemble prediction systems are uncertain due to limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter representation of ensemble forecasting systems and the corresponding observations. The framework is probabilistic, and thus allows for quantifying uncertainty in predictability measures such as correlation skill and signal-to-noise ratios. It also provides a natural way to produce recalibrated probabilistic predictions from uncalibrated ensembles f"},"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":"1504.01933","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2015-04-08T12:20:18Z","cross_cats_sorted":[],"title_canon_sha256":"9aceaba5aedebc40cab349b3321f510c48514d7b8b1363366a1d6b2cb78c0bb6","abstract_canon_sha256":"c4ff17809c588df30a757344eafbbbd30f464070a5bdfadce48f7ef55392f5fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:16:50.923001Z","signature_b64":"R0xLkT0M3E772jQLVthPZP5gteznsKf6JqiWUfSuwLL7Ckt4ZMgPG2RXHint2nTQpK/XAanlkr4wQCGvxz9gCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e5741250ab317b425200b74fd890afca62e8de485b03be6895d22a7ce4741da","last_reissued_at":"2026-05-18T01:16:50.922267Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:16:50.922267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Adam A. Scaife, Alberto Arribas, David B. Stephenson, Philip G. Sansom, Rosie Eade, Stefan Siegert","submitted_at":"2015-04-08T12:20:18Z","abstract_excerpt":"Predictability estimates of ensemble prediction systems are uncertain due to limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter representation of ensemble forecasting systems and the corresponding observations. The framework is probabilistic, and thus allows for quantifying uncertainty in predictability measures such as correlation skill and signal-to-noise ratios. It also provides a natural way to produce recalibrated probabilistic predictions from uncalibrated ensembles f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.01933","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":""},"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":"1504.01933","created_at":"2026-05-18T01:16:50.922384+00:00"},{"alias_kind":"arxiv_version","alias_value":"1504.01933v2","created_at":"2026-05-18T01:16:50.922384+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.01933","created_at":"2026-05-18T01:16:50.922384+00:00"},{"alias_kind":"pith_short_12","alias_value":"FZLUCJIKWML3","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"FZLUCJIKWML3IJJA","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"FZLUCJIK","created_at":"2026-05-18T12:29:22.688609+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/FZLUCJIKWML3IJJABN2P3CIK7S","json":"https://pith.science/pith/FZLUCJIKWML3IJJABN2P3CIK7S.json","graph_json":"https://pith.science/api/pith-number/FZLUCJIKWML3IJJABN2P3CIK7S/graph.json","events_json":"https://pith.science/api/pith-number/FZLUCJIKWML3IJJABN2P3CIK7S/events.json","paper":"https://pith.science/paper/FZLUCJIK"},"agent_actions":{"view_html":"https://pith.science/pith/FZLUCJIKWML3IJJABN2P3CIK7S","download_json":"https://pith.science/pith/FZLUCJIKWML3IJJABN2P3CIK7S.json","view_paper":"https://pith.science/paper/FZLUCJIK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1504.01933&json=true","fetch_graph":"https://pith.science/api/pith-number/FZLUCJIKWML3IJJABN2P3CIK7S/graph.json","fetch_events":"https://pith.science/api/pith-number/FZLUCJIKWML3IJJABN2P3CIK7S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FZLUCJIKWML3IJJABN2P3CIK7S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FZLUCJIKWML3IJJABN2P3CIK7S/action/storage_attestation","attest_author":"https://pith.science/pith/FZLUCJIKWML3IJJABN2P3CIK7S/action/author_attestation","sign_citation":"https://pith.science/pith/FZLUCJIKWML3IJJABN2P3CIK7S/action/citation_signature","submit_replication":"https://pith.science/pith/FZLUCJIKWML3IJJABN2P3CIK7S/action/replication_record"}},"created_at":"2026-05-18T01:16:50.922384+00:00","updated_at":"2026-05-18T01:16:50.922384+00:00"}