{"id":"W2133144322","doi":"10.1175/mwr2905.1","title":"A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems","year":2005,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":639,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data assimilation; Global Forecast System; Ensemble forecasting; Meteorology; Numerical weather prediction; North American Mesoscale Model; Weather prediction; Forecast verification; Environmental science; Computer science; Range (aeronautics); Climatology; Forecast skill; Geography; Geology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00312936,0.0003006238,0.0004690489,0.0007619849,0.0002623599,0.0008257047,0.0004258385,0.0003198162,0.0008435485],"category_scores_gemma":[0.00920706,0.0001366015,0.0002367948,0.001273781,0.00008258524,0.0008689931,0.000440533,0.0003553437,0.000190391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006253467,"about_ca_system_score_gemma":0.001236722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03999447,"about_ca_topic_score_gemma":0.03111115,"domain_scores_codex":[0.9992226,0.0003403161,0.00004758695,0.00008725112,0.000250933,0.00005130573],"domain_scores_gemma":[0.9975189,0.0009076705,0.0001750943,0.000229963,0.001091848,0.00007657685],"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":[0.0007752834,0.0001809974,0.126133,0.0001916412,0.0006580435,0.00009353223,0.0001917751,0.4813039,0.001915704,0.01005149,0.01631414,0.3621905],"study_design_scores_gemma":[0.00009418484,0.000145596,0.04991449,0.00004231404,0.0001665507,0.00003016139,0.00009570792,0.938251,0.001725866,0.00243128,0.007073494,0.00002924148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9141694,0.001532297,0.05237755,0.001032368,0.0002704596,0.0002598242,0.007940554,0.001179486,0.02123809],"genre_scores_gemma":[0.9598343,0.0006348289,0.03332799,0.0001001887,0.00006331979,0.0001724141,0.004394875,0.00005881415,0.001413306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03999447,"threshold_uncertainty_score":0.07952338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03870300239383086,"score_gpt":0.2784444181964522,"score_spread":0.2397414158026213,"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."}}