{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004398099,0.00007860711,0.0002442494,0.0000204988,0.0004439881,0.00001437288,0.0006298942,0.00003578328,0.001149534],"category_scores_gemma":[0.001331126,0.00002547219,0.0001350576,0.0005881098,0.01717416,0.00002337365,0.0001369925,0.0001646722,0.00001617906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005292238,"about_ca_system_score_gemma":0.00002983311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005331258,"about_ca_topic_score_gemma":0.000004453077,"domain_scores_codex":[0.9980605,0.0005503871,0.0002563889,0.0002487714,0.0005915365,0.0002923937],"domain_scores_gemma":[0.996182,0.003071748,0.0002051379,0.0003131548,0.0001519268,0.00007601822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002520288,0.0002668319,0.7311536,0.00003948317,0.0001952494,9.522956e-7,0.00101108,0.00137071,0.03329214,0.03711601,0.01605994,0.1769737],"study_design_scores_gemma":[0.0005874599,0.000717319,0.9600994,0.000009017882,0.00001694377,0.000002245856,0.0002684807,0.0008009635,0.0006209391,0.03200389,0.004808675,0.00006469205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848355,0.0002338016,0.0001051833,0.01070734,0.00005157141,0.0002193104,0.00004006339,0.00001222297,0.003795027],"genre_scores_gemma":[0.9977989,0.0002007223,0.00138107,0.0003731489,0.00003659174,0.000002726087,5.22266e-7,0.000001270491,0.0002050484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2289457,"threshold_uncertainty_score":0.9997635,"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."}}