{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001095815,0.0001224414,0.0001820079,0.00001445206,0.0001623389,0.00005343859,0.00004322568,0.00009421768,0.0002362716],"category_scores_gemma":[0.0000249877,0.000059319,0.0000175829,0.00002892262,0.00006133206,0.0005176743,0.0000196748,0.000132864,0.000005416218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000531666,"about_ca_system_score_gemma":0.000003118344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008784382,"about_ca_topic_score_gemma":0.00001424788,"domain_scores_codex":[0.9992813,0.00004965081,0.0002188546,0.0001648004,0.000122137,0.0001632379],"domain_scores_gemma":[0.9995744,0.00009515385,0.0001335381,0.00002679328,0.00002630675,0.0001437945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000975787,0.0003110523,0.3275767,0.0001288364,0.00006971974,0.00004360497,0.004071815,0.09429455,0.4544045,0.0003701274,0.0001931356,0.1175602],"study_design_scores_gemma":[0.0003625336,0.001520443,0.9539617,0.00001229184,0.00001697269,0.00009909696,0.0007709319,0.04185152,0.0001011984,0.001114917,0.00005134059,0.0001370253],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980279,0.001295275,0.0001071519,0.00009088931,0.00004391684,0.0001146965,0.0002565715,0.000003406677,0.00006015817],"genre_scores_gemma":[0.9908451,0.00121195,0.007443098,0.0001608774,0.00004390258,2.491415e-7,0.0002047586,0.000001306557,0.00008877748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.626385,"threshold_uncertainty_score":0.2587008,"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."}}