{"id":"W2799159667","doi":"10.1007/s13253-019-00358-2","title":"Post-processing Multiensemble Temperature and Precipitation Forecasts Through an Exchangeable Normal-Gamma Model and Its Tobit Extension","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Biological and Environmental Statistics","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydro-Québec","funders":"Électricité de France; Hydro-Québec","keywords":"Extension (predicate logic); Inference; Conditional probability distribution; Probabilistic logic; Tobit model; Probability distribution; Statistical inference; Probabilistic forecasting; Representation (politics); Statistical model","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":[0.0008472743,0.000584925,0.0004779891,0.0002787779,0.0002649124,0.0007475762,0.0005537648,0.0004205718,0.002240323],"category_scores_gemma":[0.003830072,0.0003383548,0.0003071094,0.0004752081,0.0001856342,0.001224751,0.0006335815,0.0009653773,0.0007817415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003335595,"about_ca_system_score_gemma":0.001087208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01755332,"about_ca_topic_score_gemma":0.01765815,"domain_scores_codex":[0.9997818,0.000063795,0.0000174021,0.0000468657,0.00005269705,0.00003745822],"domain_scores_gemma":[0.9989702,0.0003949993,0.00006052013,0.0001839529,0.0003250521,0.000065309],"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":[0.0008372498,0.0001539296,0.008756531,0.00005264295,0.00007849508,0.0001114945,0.00009406394,0.8793232,0.004150898,0.002157304,0.003358727,0.1009255],"study_design_scores_gemma":[0.0000106557,0.00001426938,0.0008076284,0.000001735443,0.000003949821,0.000004287303,0.00001073419,0.9972467,0.001095687,0.0005535667,0.0002462519,0.000004387759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5183173,0.0002501195,0.4694654,0.0005944999,0.0002654399,0.00005715845,0.001729103,0.007457492,0.001863498],"genre_scores_gemma":[0.9220213,0.00009148179,0.07336712,0.00005941049,0.00003294523,0.0000385265,0.001920112,0.0002650251,0.002204022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01755332,"threshold_uncertainty_score":0.03490233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02385430936788522,"score_gpt":0.2086917536594548,"score_spread":0.1848374442915696,"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."}}