{
  "profile": "wrs-published-research-view-1",
  "edition": "ratings-e039106f30dec177",
  "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS",
  "entry": {
    "id": "gencast",
    "title": "GenCast: probabilistic weather forecasting",
    "summary": "Uses machine learning to generate ensemble weather forecasts and evaluates probabilistic prediction.",
    "boundary": "Conclusions are limited to the paper’s periods, variables, regions and benchmarks. WRS has not rerun the evaluation.",
    "area": "Meteorology",
    "topic": "Earth & space",
    "year": 2024,
    "lane": "AI",
    "result": "EMPIRICAL",
    "package_id": "gencast",
    "target": {
      "record_id": "urn:uuid:d905e567-fe50-4675-acfc-431ec435fd39",
      "version": 2,
      "content_hash": "sha256:1d8625eff4fedfc01c3e24326d4d3fa13bd635983368ff1b99cb9d9a3844cb8c"
    },
    "humans": [
      "Ilan Price",
      "Alvaro Sanchez-Gonzalez",
      "Ferran Alet",
      "Tom R. Andersson",
      "Andrew El-Kadi",
      "Dominic Masters",
      "Timo Ewalds",
      "Jacklynn Stott",
      "Shakir Mohamed",
      "Peter Battaglia",
      "Remi Lam",
      "Matthew Willson"
    ],
    "authors_complete": true,
    "human_role_note": "Listed source contributors; individual labor shares are not measured",
    "agents": [
      {
        "product": "GenCast",
        "model": "GenCast",
        "provider": "Google DeepMind",
        "role": "Research system / execution",
        "assurance": "SOURCE_REPORTED",
        "source": "gencast-source"
      }
    ],
    "related": [],
    "subresults": [],
    "scientific_assessment": {
      "source_review": "SOURCE_CURATED",
      "scientific_review": "NOT_REVIEWED_BY_WRS",
      "formal_replay": "NOT_RUN",
      "provider_attestation": "NONE"
    },
    "sources": [
      {
        "id": "gencast-source",
        "title": "Price et al., Probabilistic weather forecasting with machine learning (2024)",
        "url": "https://www.nature.com/articles/s41586-024-08252-9"
      },
      {
        "id": "metadata-gencast",
        "title": "Publisher-deposited byline: Probabilistic weather forecasting with machine learning",
        "url": "https://api.crossref.org/works/10.1038%2Fs41586-024-08252-9"
      }
    ],
    "geography": {
      "country_codes": [
        "GB"
      ],
      "location_basis": "Google DeepMind, London, UK",
      "source_url": "https://europepmc.org/articles/PMC11666454",
      "target": {
        "record_id": "urn:uuid:d905e567-fe50-4675-acfc-431ec435fd39",
        "version": 2,
        "content_hash": "sha256:1d8625eff4fedfc01c3e24326d4d3fa13bd635983368ff1b99cb9d9a3844cb8c"
      },
      "basis": "RESEARCH_LOCATION_OR_PAPER_AFFILIATION",
      "reviewed_on": "2026-09-12",
      "nationality_inferred": false,
      "coverage_status": "COMPLETE",
      "country_labels": {
        "GB": "United Kingdom"
      }
    },
    "points": "14000",
    "published_grade": {
      "id": "gencast",
      "entry_id": "gencast",
      "band": "B",
      "modifier": null,
      "target": {
        "record_id": "urn:uuid:d905e567-fe50-4675-acfc-431ec435fd39",
        "version": 2,
        "content_hash": "sha256:1d8625eff4fedfc01c3e24326d4d3fa13bd635983368ff1b99cb9d9a3844cb8c"
      },
      "what_changed": "Uses machine learning to generate ensemble weather forecasts and evaluates probabilistic prediction.",
      "scope": "Uses machine learning to generate ensemble weather forecasts and evaluates probabilistic prediction. Scope limitation: Conclusions are limited to the paper’s periods, variables, regions and benchmarks. WRS has not rerun the evaluation.",
      "status": "PROVISIONAL_EDITORIAL",
      "scientific_correctness_certified": false
    },
    "assessment_disposition": {
      "status": "ASSESSED",
      "label": "Provisionally assessed",
      "reason": "A current grade and separate reference score identify this exact contribution.",
      "target": {
        "record_id": "urn:uuid:d905e567-fe50-4675-acfc-431ec435fd39",
        "version": 2,
        "content_hash": "sha256:1d8625eff4fedfc01c3e24326d4d3fa13bd635983368ff1b99cb9d9a3844cb8c"
      },
      "edition": "ratings-e039106f30dec177"
    }
  },
  "version": {
    "kind": "PUBLIC_RECORD_VIEW",
    "previous": {
      "content_hash": "sha256:9d9189abaca60e212c636b6966395ccb6236db8e0f6bd9b230b04dedd9ac695a",
      "record_id": "urn:uuid:d905e567-fe50-4675-acfc-431ec435fd39",
      "version": 1
    },
    "published_at": "2026-09-12T00:00:00Z",
    "publisher": {
      "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33",
      "role": "WRS_EDITORIAL_PUBLICATION"
    },
    "record": {
      "contributors": [],
      "evidence": [
        {
          "description": "Bibliographic/stated-result source; not a WRS proof verification",
          "id": "source-0",
          "kind": "source_reference",
          "source": {
            "citation": "Price et al., Probabilistic weather forecasting with machine learning (2024)",
            "url": "https://www.nature.com/articles/s41586-024-08252-9"
          }
        },
        {
          "description": "Bibliographic/stated-result source; not a WRS proof verification",
          "id": "source-1",
          "kind": "source_reference",
          "source": {
            "citation": "Publisher-deposited byline: Probabilistic weather forecasting with machine learning",
            "url": "https://api.crossref.org/works/10.1038%2Fs41586-024-08252-9"
          }
        }
      ],
      "origin": "AGENT_NATIVE",
      "record_profile": "wrs-local-research-notes-0.1",
      "record_type": "research_note",
      "rrp_version": "0.1",
      "scope": {
        "description": "Conclusions are limited to the paper’s periods, variables, regions and benchmarks. WRS has not rerun the evaluation.",
        "domain": "气象学"
      },
      "sources": [
        {
          "citation": "Price et al., Probabilistic weather forecasting with machine learning (2024)",
          "url": "https://www.nature.com/articles/s41586-024-08252-9"
        },
        {
          "citation": "Publisher-deposited byline: Probabilistic weather forecasting with machine learning",
          "url": "https://api.crossref.org/works/10.1038%2Fs41586-024-08252-9"
        }
      ],
      "statement": "# GenCast: probabilistic weather forecasting\n\nUses machine learning to generate ensemble weather forecasts and evaluates probabilistic prediction.\n\n## Scientific scope\n\nConclusions are limited to the paper’s periods, variables, regions and benchmarks. WRS has not rerun the evaluation.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nIlan Price; Alvaro Sanchez-Gonzalez; Ferran Alet; Tom R. Andersson; Andrew El-Kadi; Dominic Masters; Timo Ewalds; Jacklynn Stott; Shakir Mohamed; Peter Battaglia; Remi Lam; Matthew Willson\n\nListed source contributors; individual labor shares are not measured\n\nGenCast / Google DeepMind / GenCast: Research system / execution (source reported).\n\n## Original sources\n\n- [Price et al., Probabilistic weather forecasting with machine learning (2024)](https://www.nature.com/articles/s41586-024-08252-9)\n- [Publisher-deposited byline: Probabilistic weather forecasting with machine learning](https://api.crossref.org/works/10.1038%2Fs41586-024-08252-9)\n",
      "summary": "Uses machine learning to generate ensemble weather forecasts and evaluates probabilistic prediction.",
      "title": "GenCast: probabilistic weather forecasting"
    },
    "record_id": "urn:uuid:d905e567-fe50-4675-acfc-431ec435fd39",
    "version": 2,
    "source_ref": {
      "record_id": "urn:uuid:d905e567-fe50-4675-acfc-431ec435fd39",
      "version": 2,
      "content_hash": "sha256:1d8625eff4fedfc01c3e24326d4d3fa13bd635983368ff1b99cb9d9a3844cb8c"
    },
    "export_view": "wrs-public-editorial-view-1",
    "public_view_hash": "sha256:31718a6d5510522cff4e1c3fd74fd1717b6a2a00e6f6a2c0c6b3a86472333afb"
  },
  "target": {
    "record_id": "urn:uuid:d905e567-fe50-4675-acfc-431ec435fd39",
    "version": 2,
    "content_hash": "sha256:1d8625eff4fedfc01c3e24326d4d3fa13bd635983368ff1b99cb9d9a3844cb8c"
  },
  "published_grade": {
    "profile": "wrs-published-grade-1",
    "edition": "2026-10-28.1",
    "id": "gencast",
    "entry_id": "gencast",
    "revision": 1,
    "title": "GenCast: probabilistic weather forecasting",
    "context": "Meteorology",
    "track": "Scoped scientific or research-capability contribution",
    "band": "B",
    "modifier": null,
    "status": "PROVISIONAL_EDITORIAL",
    "assessed_on": "2026-09-14",
    "evidence_cutoff": "2026-09-14",
    "achievement_state": "SUPPORTED_SCOPED_RESULT",
    "what_changed": "Uses machine learning to generate ensemble weather forecasts and evaluates probabilistic prediction.",
    "scope": "Uses machine learning to generate ensemble weather forecasts and evaluates probabilistic prediction. Scope limitation: Conclusions are limited to the paper’s periods, variables, regions and benchmarks. WRS has not rerun the evaluation.",
    "evidence_summary": "Assessment uses the catalogue-linked source evidence for the stated contribution. Conclusions are limited to the paper’s periods, variables, regions and benchmarks. WRS has not rerun the evaluation.",
    "references": [
      {
        "title": "Price et al., Probabilistic weather forecasting with machine learning (2024)",
        "url": "https://www.nature.com/articles/s41586-024-08252-9",
        "locator": "Retained catalogue source evidence; Bibliography and stated result; no full proof audit"
      },
      {
        "title": "Publisher-deposited byline: Probabilistic weather forecasting with machine learning",
        "url": "https://api.crossref.org/works/10.1038%2Fs41586-024-08252-9",
        "locator": "Retained catalogue source evidence; Complete deposited byline retained locally with a checksum; bibliographic evidence only."
      }
    ],
    "roles": [
      {
        "who": "The research collaboration listed in the linked record and source byline",
        "credit": "SCIENCE",
        "did": "Collective scoped contribution. The complete recorded byline remains in the research record; this label does not collapse people into one individual."
      },
      {
        "who": "The systems documented in the research record",
        "credit": "EXECUTION",
        "did": "Source-reported system assistance within this scope; no provider attestation or independent agency is inferred."
      }
    ],
    "target": {
      "record_id": "urn:uuid:d905e567-fe50-4675-acfc-431ec435fd39",
      "version": 2,
      "content_hash": "sha256:1d8625eff4fedfc01c3e24326d4d3fa13bd635983368ff1b99cb9d9a3844cb8c"
    },
    "scientific_correctness_certified": false,
    "independent_expert_calibration": false,
    "method": "PROPRIETARY",
    "points": "14000"
  },
  "canonical_agent_submission": false,
  "scientific_verification_awarded": false,
  "reader_guide": null,
  "reading_status": "SOURCE_LINKED_NOTE"
}
