{"id":"W2145980277","doi":"10.1175/2009mwr3187.1","title":"Toward Random Sampling of Model Error in the Canadian Ensemble Prediction System","year":2009,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Kalman filter; Probabilistic logic; Ensemble Kalman filter; Ensemble forecasting; Meteorology; Computer science; Sensitivity (control systems); Environmental science; Grid; Sampling (signal processing); Precipitation; Filter (signal processing); Extended Kalman filter; Geology; Geodesy; Machine learning; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01039314,0.0007914444,0.001107903,0.001089971,0.001213292,0.001303276,0.00173991,0.0007250686,0.0005431048],"category_scores_gemma":[0.03388062,0.0005091174,0.0004224404,0.001020636,0.0009999064,0.001085533,0.001415348,0.001234917,0.00008831766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005418948,"about_ca_system_score_gemma":0.009382728,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7459718,"about_ca_topic_score_gemma":0.5640748,"domain_scores_codex":[0.9972321,0.00146778,0.00009255383,0.0002985188,0.0006268687,0.0002821154],"domain_scores_gemma":[0.9875529,0.006684519,0.0006923427,0.0007799161,0.00398274,0.0003075685],"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":[0.0001686314,0.00002687775,0.00655855,0.00001749332,0.00006741259,0.00002319638,0.00004323747,0.9731978,0.0002716292,0.005225487,0.0004700355,0.0139297],"study_design_scores_gemma":[0.00000707568,0.000004660021,0.0005235966,0.000002111855,0.000003233991,0.000001319778,0.000003583233,0.9987778,0.00009918579,0.0005159391,0.00005777384,0.000003628996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5355074,0.0007826214,0.4553481,0.0009911569,0.000111748,0.0002421472,0.0006350519,0.0009344951,0.00544717],"genre_scores_gemma":[0.9532151,0.0001372476,0.04529641,0.00007414574,0.0000284921,0.00007532385,0.0003771025,0.00004781095,0.0007483546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2540282,"threshold_uncertainty_score":0.5110484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09555955018991234,"score_gpt":0.2707696327940995,"score_spread":0.1752100826041872,"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."}}