{"id":"W4235952653","doi":"10.5194/hessd-6-4891-2009","title":"An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting","year":2009,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ensemble forecasting; Consensus forecast; Meteorology; Quantitative precipitation forecast; Ensemble average; Forecast verification; Global Forecast System; Environmental science; Streamflow; Term (time); Climatology; Econometrics; Computer science; Forecast skill; Numerical weather prediction; Mathematics; Precipitation; Geography; Geology","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":[0.003276083,0.001138703,0.0006502991,0.0009607176,0.001261856,0.0009928306,0.001230732,0.0006194846,0.001369616],"category_scores_gemma":[0.006934493,0.0002368725,0.0004532945,0.001412677,0.0002277988,0.0009187055,0.0007801363,0.0006057693,0.0002496302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005568058,"about_ca_system_score_gemma":0.008530329,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8869304,"about_ca_topic_score_gemma":0.8325958,"domain_scores_codex":[0.9991221,0.0002285796,0.00004136664,0.0001374074,0.0003705839,0.0001000313],"domain_scores_gemma":[0.9970948,0.0004581009,0.00007923388,0.0001320679,0.002010575,0.0002252733],"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":[0.00164225,0.0007560183,0.09834257,0.0002752287,0.0008646648,0.0002392686,0.0002028303,0.6504908,0.00337951,0.001837122,0.01707933,0.2248905],"study_design_scores_gemma":[0.00006706214,0.0001217996,0.02562499,0.00001448656,0.00009348292,0.00001149749,0.00006231731,0.9716647,0.0008295068,0.0001299987,0.001348779,0.00003140209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9568185,0.001145597,0.02101252,0.001000564,0.0003401425,0.0004590035,0.006453956,0.002008399,0.01076126],"genre_scores_gemma":[0.9737139,0.0004648218,0.01749816,0.0001001283,0.000033425,0.0001511293,0.006175502,0.00006447914,0.001798384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1130696,"threshold_uncertainty_score":0.2274709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06958794094934596,"score_gpt":0.2849652081226752,"score_spread":0.2153772671733292,"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."}}