{"id":"W2166321511","doi":"10.5194/hess-13-2221-2009","title":"An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting","year":2009,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"GDG Environnement","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Consejo Nacional de Ciencia y Tecnología","keywords":"Ensemble forecasting; Quantitative precipitation forecast; Ensemble average; Consensus forecast; Meteorology; Forecast verification; Global Forecast System; Streamflow; Environmental science; Term (time); Climatology; Lead time; Computer science; Forecast skill; Econometrics; Numerical weather prediction; Precipitation; Mathematics; Geology; Geography; Economics; Drainage basin","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":[0.003330701,0.001157047,0.0006478584,0.0009715608,0.001254292,0.0009803099,0.001236392,0.0006172895,0.001409717],"category_scores_gemma":[0.006982924,0.0002394968,0.0004687206,0.001395944,0.0002253609,0.0009050558,0.0007837606,0.000602593,0.0002624802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005645543,"about_ca_system_score_gemma":0.008689345,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8900383,"about_ca_topic_score_gemma":0.8344168,"domain_scores_codex":[0.9991259,0.000228703,0.00004110458,0.0001392907,0.0003628025,0.0001022145],"domain_scores_gemma":[0.9970375,0.000451413,0.00007945906,0.0001331898,0.002073367,0.0002250079],"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":[0.001670052,0.0007098526,0.09710019,0.0002900687,0.0008916036,0.0002392977,0.0001961234,0.6562682,0.003347579,0.001894307,0.01800958,0.2193831],"study_design_scores_gemma":[0.00006709409,0.0001170094,0.02546813,0.00001527973,0.00009561928,0.00001136159,0.00006020628,0.9718217,0.0008216265,0.0001307921,0.001359534,0.00003172572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9554476,0.001213013,0.0212404,0.001042123,0.0003611065,0.0004573852,0.007213977,0.002085203,0.01093922],"genre_scores_gemma":[0.9730584,0.0004743146,0.01750737,0.0001054058,0.00003464672,0.0001510259,0.006731579,0.00006728245,0.001869886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1099617,"threshold_uncertainty_score":0.2212186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05234870394530464,"score_gpt":0.2675458393126252,"score_spread":0.2151971353673206,"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."}}