{"id":"W2161543129","doi":"10.1111/rssc.12062","title":"Combining the Bayesian Processor of Output with Bayesian Model Averaging for Reliable Ensemble Forecasting","year":2014,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"GDG Environnement; Université Laval","funders":"Mitacs; Hydro-Québec; Manitoba Hydro; Université Laval","keywords":"Bayesian probability; Ensemble forecasting; Computer science; Bayesian inference; Bayesian average; Ensemble learning; Bayesian statistics; Statistical ensemble; Set (abstract data type); Machine learning; Artificial intelligence; Statistics; Monte Carlo method; Mathematics; Canonical ensemble","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001014324,0.000201015,0.0004338161,0.00001707768,0.0007281736,0.00009571449,0.0004125264,0.00007281126,0.0001271179],"category_scores_gemma":[0.0004781774,0.0001018205,0.0001158694,0.0001471259,0.0003723574,0.00008824429,0.00003073297,0.000363182,0.00000126243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001537777,"about_ca_system_score_gemma":0.0001258121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004541629,"about_ca_topic_score_gemma":0.00007641128,"domain_scores_codex":[0.9981306,0.00007852227,0.0006681033,0.0001904597,0.0005268186,0.0004055149],"domain_scores_gemma":[0.9966613,0.002124551,0.0006255545,0.000183997,0.0002469803,0.0001576108],"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":[0.000480204,0.00004133159,0.003577311,0.0001567617,0.000116968,0.000001274324,0.001151879,0.9033732,0.00001928414,0.07750537,0.002979603,0.01059677],"study_design_scores_gemma":[0.0005916713,0.000449286,0.003303627,0.00003147902,0.0001274764,0.000006377798,0.0004358929,0.8474503,0.00002619404,0.1468523,0.0005889609,0.0001365159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005803906,0.00002894342,0.9900542,0.0004055789,0.0001152443,0.0003073857,0.0004250667,0.00001009526,0.002849501],"genre_scores_gemma":[0.7094268,0.000003259239,0.2899968,0.0002527145,0.00007731704,0.000002957172,0.00002008703,0.000008856362,0.0002112122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7036229,"threshold_uncertainty_score":0.5600597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02010567577204316,"score_gpt":0.2145050667762668,"score_spread":0.1943993910042237,"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."}}