{"id":"W3166273424","doi":"10.1016/j.jhydrol.2021.126537","title":"Hydrological ensemble forecasting using a multi-model framework","year":2021,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ensemble forecasting; Hydrological modelling; Computer science; Meteorology; Environmental science; Climatology; Geology; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005398392,0.0001428905,0.0003513829,0.00005545377,0.0001966054,0.00001171043,0.0002162815,0.0001851275,0.0005219608],"category_scores_gemma":[0.0003225719,0.0001147629,0.0001418789,0.0001579386,0.0002406373,0.0001619687,0.0003970884,0.0004448284,0.00005545596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006860548,"about_ca_system_score_gemma":0.00001810153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007336276,"about_ca_topic_score_gemma":0.00001945248,"domain_scores_codex":[0.9986296,0.0001475071,0.000424606,0.0002237512,0.0001841824,0.0003903018],"domain_scores_gemma":[0.9993113,0.0001382287,0.0002816618,0.0001554509,0.00002364254,0.00008972245],"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":[0.000172286,0.0003929555,0.1338697,0.00001162873,0.00022301,0.002431681,0.001031902,0.833886,0.02516891,0.0004189078,0.001015358,0.001377619],"study_design_scores_gemma":[0.001230508,0.0004210432,0.003944241,0.00002877611,0.0002079036,0.002516205,0.000103017,0.9465609,0.002540829,0.03932094,0.002822972,0.0003027085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8924422,0.0001726292,0.1036652,0.001504481,0.000252912,0.00004233026,5.051525e-7,0.0000110914,0.001908724],"genre_scores_gemma":[0.9287808,0.00004727178,0.06890485,0.001977807,0.00008407654,0.000001265186,4.183378e-7,0.000009963704,0.0001935707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1299255,"threshold_uncertainty_score":0.5715105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06523747026125477,"score_gpt":0.2856613860591253,"score_spread":0.2204239157978705,"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."}}