{"id":"W2521029069","doi":"10.1175/bams-d-14-00025.1","title":"THORPEX Research and the Science of Prediction","year":2016,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; University of Waterloo; Environment and Climate Change Canada","funders":"National Aeronautics and Space Administration; National Oceanic and Atmospheric Administration; Met Office; National Science Foundation","keywords":"Predictability; Weather prediction; Research program; Numerical weather prediction; Data assimilation; Weather forecasting; Meteorology; Atmospheric research; Computer science; Operations research; Climatology; Environmental science; Geography; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009896064,0.001106972,0.0008771452,0.001908772,0.002027875,0.008086276,0.001343651,0.003441296,0.01928883],"category_scores_gemma":[0.02896023,0.0003644082,0.0006087706,0.002221005,0.005669731,0.009796686,0.004977731,0.007332935,0.006327288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004554081,"about_ca_system_score_gemma":0.007500717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01030712,"about_ca_topic_score_gemma":0.00345021,"domain_scores_codex":[0.9944853,0.00257039,0.0002660228,0.0009999025,0.001427203,0.0002510591],"domain_scores_gemma":[0.9829102,0.007688192,0.0009871424,0.002983668,0.004535934,0.0008947829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008536864,0.00003541539,0.001341509,0.0001865058,0.00002982127,0.00007385905,0.000248771,0.005330198,0.0002085156,0.7285753,0.174443,0.08944166],"study_design_scores_gemma":[0.00003910103,0.00004895013,0.001068003,0.0008063192,0.00001467962,0.00005522607,0.0002848149,0.01397409,0.0006783707,0.3991144,0.5838664,0.00004971633],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.009746616,0.08711547,0.06132892,0.5220043,0.03219853,0.0001329404,0.003531414,0.001759547,0.2821822],"genre_scores_gemma":[0.5105345,0.1255447,0.09791954,0.03265472,0.02102451,0.0006649691,0.006181943,0.002351616,0.2031236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01928883,"threshold_uncertainty_score":0.06452757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04036253969341941,"score_gpt":0.2756733625117153,"score_spread":0.2353108228182959,"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."}}