{"id":"W2112268710","doi":"10.1175/mwr-d-12-00206.1","title":"A Comparison of the Canadian Global and Regional Meteorological Ensemble Prediction Systems for Short-Term Hydrological Forecasting","year":2013,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"GDG Environnement; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ensemble forecasting; Term (time); Meteorology; Ensemble average; Environmental science; Reliability (semiconductor); Computer science; Mean squared error; Forecast verification; North American Mesoscale Model; Forecast skill; Global Forecast System; Numerical weather prediction; Climatology; Statistics; Mathematics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003360991,0.0001132501,0.000288844,0.000007417706,0.0002498172,0.00001302236,0.0001593107,0.00007434304,0.00009080939],"category_scores_gemma":[0.0000391137,0.00006384514,0.00007247326,0.00007055377,0.0002234158,0.00006451679,0.0001185741,0.00006739271,0.00001493927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008232597,"about_ca_system_score_gemma":0.000004509848,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008105171,"about_ca_topic_score_gemma":0.01311738,"domain_scores_codex":[0.9990587,0.000100361,0.0002575506,0.0002217761,0.0001266266,0.0002349492],"domain_scores_gemma":[0.9996576,0.00004007911,0.00007423021,0.0001480188,0.000008882233,0.00007119367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007805611,0.00004258524,0.9721463,0.0002554104,0.00005342435,0.000001123109,0.00009170795,0.0006861851,0.00002673785,0.0003258992,0.01968717,0.006675657],"study_design_scores_gemma":[0.0004534491,0.0005847967,0.7931165,0.0007897781,0.0004393079,0.0000259081,0.00007417962,0.05041246,0.00001434553,0.002380476,0.1513521,0.000356702],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9171495,0.0543734,0.0002050728,0.006456729,0.0002094187,0.003862543,0.00003717478,0.00004091726,0.01766523],"genre_scores_gemma":[0.9979917,0.0008146336,0.0001016426,0.0006797903,0.0000172756,0.0002928622,0.000005003506,0.00000383686,0.00009320194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1790298,"threshold_uncertainty_score":0.9984999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06532088050031401,"score_gpt":0.2779573884027006,"score_spread":0.2126365079023866,"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."}}