{"id":"W4226217653","doi":"10.5194/hess-26-1001-2022","title":"Exploring hydrologic post-processing of ensemble streamflow forecasts based on affine kernel dressing and non-dominated sorting genetic algorithm II","year":2022,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université Laval","funders":"","keywords":"Sorting; Computer science; Ensemble forecasting; Genetic algorithm; Kernel (algebra); Parametric statistics; Streamflow; Feature (linguistics); Data mining; Algorithm; Mathematical optimization; Machine learning; Mathematics; Statistics; Drainage basin","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":[0.0006855939,0.0004096903,0.0004402228,0.0004827865,0.0002723397,0.0005269553,0.0004941713,0.0005161208,0.0007310201],"category_scores_gemma":[0.001344581,0.0001877163,0.0003291257,0.0003705062,0.0002093626,0.0004412389,0.0004613274,0.0005441526,0.00009362351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005641373,"about_ca_system_score_gemma":0.001147188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006062712,"about_ca_topic_score_gemma":0.006032012,"domain_scores_codex":[0.9998019,0.00005033349,0.00001052563,0.000034203,0.00006530787,0.00003769944],"domain_scores_gemma":[0.9995003,0.0002336802,0.00005315569,0.00003069043,0.0001475853,0.00003456671],"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":[0.0001013818,0.0001018485,0.001701943,0.00002120807,0.00002578538,0.00004426039,0.00005023751,0.9097602,0.005315539,0.001605348,0.0002695797,0.08100266],"study_design_scores_gemma":[0.000002514895,0.00001708963,0.000124225,6.555916e-7,0.000001930522,0.000002170221,0.000003244685,0.9989805,0.0006802521,0.0001423802,0.00004407524,0.000001019341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4472868,0.0001189517,0.5492002,0.0001421202,0.00005109071,0.00007360557,0.00003808863,0.0005510897,0.002538057],"genre_scores_gemma":[0.9040784,0.00004151553,0.09469938,0.00004165791,0.00001007849,0.00005543452,0.0000606937,0.00002403867,0.0009886732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006062712,"threshold_uncertainty_score":0.01205486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02658781889481163,"score_gpt":0.2145112158499514,"score_spread":0.1879233969551398,"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."}}