{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3IG7EDCO7HCJ57BISP62SFJ7JJ","short_pith_number":"pith:3IG7EDCO","schema_version":"1.0","canonical_sha256":"da0df20c4ef9c49efc2893fda9153f4a50161db31294a7334eab7634862f406f","source":{"kind":"arxiv","id":"2509.06227","version":1},"attestation_state":"computed","paper":{"title":"Distillation of CNN Ensemble Results for Enhanced Long-Term Prediction of the ENSO Phenomenon","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.CE","physics.app-ph"],"primary_cat":"physics.ao-ph","authors_text":"Alireza Hassani, Arash Adib, Mohammad Naisipour, Saghar Ganji","submitted_at":"2025-09-07T22:26:42Z","abstract_excerpt":"The accurate long-term forecasting of the El Nino Southern Oscillation (ENSO) is still one of the biggest challenges in climate science. While it is true that short-to medium-range performance has been improved significantly using the advances in deep learning, statistical dynamical hybrids, most operational systems still use the simple mean of all ensemble members, implicitly assuming equal skill across members. In this study, we demonstrate, through a strictly a-posteriori evaluation , for any large enough ensemble of ENSO forecasts, there is a subset of members whose skill is substantially "},"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":"2509.06227","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"physics.ao-ph","submitted_at":"2025-09-07T22:26:42Z","cross_cats_sorted":["cs.AI","cs.CE","physics.app-ph"],"title_canon_sha256":"0d5bb838a13e0771d5585f1319cb989fc8974cbb22f06fa6d8c3d6493f2e0cff","abstract_canon_sha256":"22b359f7d5cbc47b9bec83de4889b8bbae971ddcec316232f91cfa68e517572b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:32.056210Z","signature_b64":"ufgVaC0wmo7Nl18qTvTvM35o3Os2Xfu6adNnK3zWyAAfXZ/8u2Rl4biAWaWsA5/+UVq/vnBHJFoyba6pBrZ3AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da0df20c4ef9c49efc2893fda9153f4a50161db31294a7334eab7634862f406f","last_reissued_at":"2026-07-05T12:06:32.055696Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:32.055696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distillation of CNN Ensemble Results for Enhanced Long-Term Prediction of the ENSO Phenomenon","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.CE","physics.app-ph"],"primary_cat":"physics.ao-ph","authors_text":"Alireza Hassani, Arash Adib, Mohammad Naisipour, Saghar Ganji","submitted_at":"2025-09-07T22:26:42Z","abstract_excerpt":"The accurate long-term forecasting of the El Nino Southern Oscillation (ENSO) is still one of the biggest challenges in climate science. While it is true that short-to medium-range performance has been improved significantly using the advances in deep learning, statistical dynamical hybrids, most operational systems still use the simple mean of all ensemble members, implicitly assuming equal skill across members. In this study, we demonstrate, through a strictly a-posteriori evaluation , for any large enough ensemble of ENSO forecasts, there is a subset of members whose skill is substantially "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.06227","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/2509.06227/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":"2509.06227","created_at":"2026-07-05T12:06:32.055774+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.06227v1","created_at":"2026-07-05T12:06:32.055774+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.06227","created_at":"2026-07-05T12:06:32.055774+00:00"},{"alias_kind":"pith_short_12","alias_value":"3IG7EDCO7HCJ","created_at":"2026-07-05T12:06:32.055774+00:00"},{"alias_kind":"pith_short_16","alias_value":"3IG7EDCO7HCJ57BI","created_at":"2026-07-05T12:06:32.055774+00:00"},{"alias_kind":"pith_short_8","alias_value":"3IG7EDCO","created_at":"2026-07-05T12:06:32.055774+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/3IG7EDCO7HCJ57BISP62SFJ7JJ","json":"https://pith.science/pith/3IG7EDCO7HCJ57BISP62SFJ7JJ.json","graph_json":"https://pith.science/api/pith-number/3IG7EDCO7HCJ57BISP62SFJ7JJ/graph.json","events_json":"https://pith.science/api/pith-number/3IG7EDCO7HCJ57BISP62SFJ7JJ/events.json","paper":"https://pith.science/paper/3IG7EDCO"},"agent_actions":{"view_html":"https://pith.science/pith/3IG7EDCO7HCJ57BISP62SFJ7JJ","download_json":"https://pith.science/pith/3IG7EDCO7HCJ57BISP62SFJ7JJ.json","view_paper":"https://pith.science/paper/3IG7EDCO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.06227&json=true","fetch_graph":"https://pith.science/api/pith-number/3IG7EDCO7HCJ57BISP62SFJ7JJ/graph.json","fetch_events":"https://pith.science/api/pith-number/3IG7EDCO7HCJ57BISP62SFJ7JJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3IG7EDCO7HCJ57BISP62SFJ7JJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3IG7EDCO7HCJ57BISP62SFJ7JJ/action/storage_attestation","attest_author":"https://pith.science/pith/3IG7EDCO7HCJ57BISP62SFJ7JJ/action/author_attestation","sign_citation":"https://pith.science/pith/3IG7EDCO7HCJ57BISP62SFJ7JJ/action/citation_signature","submit_replication":"https://pith.science/pith/3IG7EDCO7HCJ57BISP62SFJ7JJ/action/replication_record"}},"created_at":"2026-07-05T12:06:32.055774+00:00","updated_at":"2026-07-05T12:06:32.055774+00:00"}