{"id":"W2137527689","doi":"10.1256/qj.05.167","title":"Probabilistic forecasting from ensemble prediction systems: Improving upon the best‐member method by using a different weight and dressing kernel for each member","year":2006,"lang":"en","type":"article","venue":"Quarterly Journal of the Royal Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Institut National de la Recherche Scientifique; Environment and Climate Change Canada","funders":"","keywords":"Weighting; Computer science; Probabilistic logic; Ensemble forecasting; Ensemble learning; Resampling; Statistic; Variance (accounting); Kernel (algebra); Artificial intelligence; Data mining; Machine learning; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001516299,0.0002460145,0.0004452107,0.00001704413,0.000855404,0.0002433671,0.0003129783,0.0002074526,0.00008234653],"category_scores_gemma":[0.0001570471,0.0001088023,0.000379225,0.0001019094,0.0001419317,0.0001608994,0.00002428745,0.0004486431,8.745225e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003446149,"about_ca_system_score_gemma":0.00002712632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009957702,"about_ca_topic_score_gemma":0.00007733605,"domain_scores_codex":[0.9974355,0.0007001192,0.0007457713,0.0003017108,0.000405134,0.0004117311],"domain_scores_gemma":[0.9967197,0.00221488,0.0006555232,0.0001722906,0.0001156056,0.0001220307],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007941891,0.0004483338,0.1920109,0.0002090151,0.000712632,0.000009427708,0.001877732,0.687583,0.01012393,0.0003206333,0.001834314,0.1040759],"study_design_scores_gemma":[0.0006829553,0.0007994986,0.01866524,0.00004246959,0.0003517882,0.00002588253,0.0004668628,0.9669324,0.00003834475,0.01159411,0.0002503293,0.0001500938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9369797,0.002125444,0.05945159,0.0002222779,0.0005197238,0.0004555105,0.0001144415,0.00001638384,0.0001149841],"genre_scores_gemma":[0.9911864,0.000003580247,0.008012366,0.0000944892,0.0006010732,0.000005311016,0.00001586695,0.000007358286,0.00007350573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2793494,"threshold_uncertainty_score":0.6579162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0395667087528634,"score_gpt":0.2478631562234109,"score_spread":0.2082964474705475,"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."}}