{"id":"W1928286853","doi":"10.1002/hyp.10234","title":"Exploratory analysis of statistical post‐processing methods for hydrological ensemble forecasts","year":2014,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Hydro-Québec; Université du Québec à Chicoutimi","funders":"","keywords":"Streamflow; Computer science; Calibration; Missing data; Regression; Raw data; Data mining; Statistics; Machine learning; Mathematics","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":[0.01588222,0.000598794,0.0003931792,0.001619154,0.0003570249,0.0009582978,0.0004983245,0.0003979545,0.001639186],"category_scores_gemma":[0.04589192,0.0002318645,0.0009600519,0.001100947,0.0002980946,0.001181046,0.0007182923,0.0008840953,0.0002232441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003997954,"about_ca_system_score_gemma":0.0006802804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001296692,"about_ca_topic_score_gemma":0.001143152,"domain_scores_codex":[0.9951691,0.003285273,0.0002321631,0.0002218969,0.0009714296,0.0001201557],"domain_scores_gemma":[0.9089673,0.07927973,0.002426071,0.002834672,0.006216134,0.0002761097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001595272,0.0008406605,0.09006284,0.0005733162,0.001132543,0.0003501174,0.001099925,0.3081543,0.01729657,0.01829831,0.003935286,0.5566608],"study_design_scores_gemma":[0.00002516191,0.0003144071,0.02025667,0.00004108849,0.00005465187,0.00004715394,0.000157147,0.9686736,0.005717445,0.003635602,0.001039081,0.00003802783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4020447,0.0003440086,0.5939817,0.0002878488,0.00006133552,0.0003117918,0.0005395835,0.001003652,0.001425482],"genre_scores_gemma":[0.8020967,0.00009685425,0.1961824,0.0000267754,0.00002780812,0.0002968692,0.0007759028,0.0001594642,0.0003373679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01588222,"threshold_uncertainty_score":0.08399421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150587605510279,"score_gpt":0.3239359953737599,"score_spread":0.2924301193186571,"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."}}