{"id":"W1964349537","doi":"10.2166/hydro.2009.037","title":"Tools for the assessment of hydrological ensemble forecasts obtained by neural networks","year":2009,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Université Laval","funders":"","keywords":"Probabilistic forecasting; Ensemble forecasting; Probabilistic logic; Computer science; Streamflow; Artificial neural network; Point (geometry); Process (computing); Consensus forecast; Range (aeronautics); Econometrics; Meteorology; Artificial intelligence; Mathematics; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.007427487,0.001248527,0.000889782,0.004222046,0.0003674946,0.001923191,0.0007020858,0.00105828,0.001425999],"category_scores_gemma":[0.03157984,0.000325149,0.0007735448,0.001793323,0.0007822973,0.002165959,0.0015748,0.001040779,0.0002758111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000889551,"about_ca_system_score_gemma":0.0006748317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003271348,"about_ca_topic_score_gemma":0.002052283,"domain_scores_codex":[0.9978766,0.0008276053,0.0002341846,0.0001579678,0.0007981177,0.0001054621],"domain_scores_gemma":[0.9874999,0.009182568,0.001360461,0.0006080616,0.001225395,0.0001237086],"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":[0.0001321235,0.00005410746,0.004325046,0.0001436114,0.0001465034,0.0001274632,0.0001151872,0.8573992,0.003041427,0.02785602,0.0006884262,0.1059708],"study_design_scores_gemma":[0.000004788134,0.00002612943,0.0009858937,0.00002305173,0.00001398541,0.00002404666,0.00001684393,0.9873029,0.0009747581,0.0103162,0.0002952566,0.00001612834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03836083,0.000537299,0.9584481,0.0001336082,0.0000246142,0.0000582213,0.0001933992,0.0006114714,0.001632488],"genre_scores_gemma":[0.7019736,0.0007585957,0.295383,0.00004547253,0.00008241034,0.0002675431,0.000550601,0.0001701526,0.0007686427],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007427487,"threshold_uncertainty_score":0.03928077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01680828778967735,"score_gpt":0.2592327988130455,"score_spread":0.2424245110233681,"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."}}