{"id":"W2614963984","doi":"10.1061/9780784480618.038","title":"Model Selection Tools for Hydrological Frequency Analysis: Some New Results","year":2017,"lang":"en","type":"article","venue":"World Environmental and Water Resources Congress 2017","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Akaike information criterion; Bayesian information criterion; Statistic; Goodness of fit; Statistics; Model selection; Bayesian probability; Probability distribution; Computer science; Plot (graphics); Focus (optics); Selection (genetic algorithm); Mathematics; Econometrics; Data mining; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01756857,0.002713453,0.002326346,0.004445044,0.0008031447,0.002646289,0.002363748,0.001923238,0.005142523],"category_scores_gemma":[0.05249938,0.001166609,0.004862646,0.00475682,0.002009814,0.004476367,0.002984371,0.006123439,0.001571743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009096763,"about_ca_system_score_gemma":0.001004899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003611249,"about_ca_topic_score_gemma":0.002348259,"domain_scores_codex":[0.9933757,0.004405959,0.0004363464,0.0004884523,0.001163087,0.000130346],"domain_scores_gemma":[0.9596159,0.03526849,0.0009069954,0.001949364,0.001947485,0.0003117221],"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":[0.0001097814,0.0004283666,0.005705545,0.001222619,0.001019056,0.0009870663,0.0006652831,0.1700674,0.001287366,0.2392643,0.02166747,0.5575759],"study_design_scores_gemma":[0.0000569078,0.0001711723,0.001605638,0.0005854742,0.0001757183,0.0004432798,0.0001317709,0.6791022,0.0007029937,0.2782577,0.03861615,0.0001510017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001841133,0.01463797,0.977703,0.002762278,0.0003535945,0.00004466638,0.000128403,0.0007294535,0.001799479],"genre_scores_gemma":[0.04846973,0.0292645,0.9113668,0.001635379,0.003990108,0.0004861579,0.000618313,0.0009165952,0.003252498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01756857,"threshold_uncertainty_score":0.09291261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02556289065155873,"score_gpt":0.2405980310315613,"score_spread":0.2150351403800026,"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."}}