{"id":"W2161008859","doi":"10.1175/2009mwr3154.1","title":"Improving Seasonal Forecast Skill of North American Surface Air Temperature in Fall Using a Postprocessing Method","year":2009,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Climate variability and models","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Geopotential height; Climatology; Forecast skill; Environmental science; Standard deviation; General Circulation Model; Ensemble average; Singular value decomposition; Geopotential; Meteorology; Surface air temperature; Mathematics; Statistics; Precipitation; Geology; Geography; Climate change","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007483014,0.0004502244,0.0003449486,0.00085753,0.0002542904,0.0006153381,0.0003475245,0.0002035444,0.001002816],"category_scores_gemma":[0.002629617,0.0001823155,0.0004076573,0.000679038,0.0001610681,0.0004269559,0.0003082061,0.000380306,0.0002666833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003827027,"about_ca_system_score_gemma":0.0008144105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01382488,"about_ca_topic_score_gemma":0.01361535,"domain_scores_codex":[0.999795,0.00004864152,0.00001795281,0.00005720357,0.00005576793,0.00002538918],"domain_scores_gemma":[0.9988928,0.0004800628,0.0001246257,0.0001025299,0.000365873,0.00003401911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007753922,0.0003273989,0.0708868,0.00007687508,0.0001584045,0.0001443266,0.0001958513,0.2165796,0.04927759,0.001580132,0.003416396,0.6565812],"study_design_scores_gemma":[0.00003740612,0.0001334499,0.04201578,0.000007091116,0.0000606435,0.00003810371,0.00004099089,0.9334393,0.02204889,0.0008104177,0.001348776,0.00001919402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7727186,0.0001924245,0.2216211,0.0003061676,0.0000992335,0.00006114146,0.0005819272,0.002208816,0.002210547],"genre_scores_gemma":[0.8691103,0.00007018629,0.129187,0.00003389123,0.00005684987,0.00004097196,0.0007677602,0.00009266075,0.0006404345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01382488,"threshold_uncertainty_score":0.02748883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646218598001244,"score_gpt":0.2804021161803884,"score_spread":0.263939930200376,"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."}}