{"id":"W4298138444","doi":"10.1175/bams-d-22-0046.1","title":"Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence","year":2022,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal; Environment and Climate Change Canada","funders":"Office of Naval Research; European Centre for Medium-Range Weather Forecasts; San Diego Supercomputer Center; U.S. Naval Research Laboratory; Ministère de l'Économie, de l’Innovation et des Exportations du Québec; University of Connecticut","keywords":"Quantitative precipitation forecast; Precipitation; Meteorology; Climatology; Environmental science; Calibration; Forecast skill; Weather prediction; Computer science; Raw data; Statistics; Geography; Mathematics; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02288852,0.001821709,0.000946883,0.001517367,0.001851842,0.005341936,0.001374281,0.002376372,0.007191493],"category_scores_gemma":[0.02043054,0.0003081823,0.0008878967,0.001222074,0.001114134,0.001578647,0.003469818,0.003044813,0.002641235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00205064,"about_ca_system_score_gemma":0.005176384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01751,"about_ca_topic_score_gemma":0.01354109,"domain_scores_codex":[0.9954895,0.001346148,0.0001272735,0.0003483591,0.002146688,0.0005420923],"domain_scores_gemma":[0.9799259,0.003797687,0.0007739316,0.0008857751,0.00842955,0.006187061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001170718,0.0004811267,0.005250768,0.0003147399,0.0002930209,0.0006123062,0.0002457601,0.03480464,0.004452028,0.007026784,0.879681,0.06566703],"study_design_scores_gemma":[0.001243804,0.001316741,0.04152076,0.0003706229,0.0001954896,0.0002252914,0.001101587,0.2304111,0.01957198,0.03933685,0.6643819,0.0003237798],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3657735,0.01492032,0.04825073,0.2388501,0.1505482,0.001278051,0.04810619,0.005521701,0.1267512],"genre_scores_gemma":[0.7976394,0.004146595,0.03275491,0.00967512,0.01464908,0.0006924431,0.04385847,0.002336049,0.09424789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02288852,"threshold_uncertainty_score":0.1210474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04149558496810211,"score_gpt":0.2612863743390768,"score_spread":0.2197907893709747,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}