{"id":"W2224252211","doi":"10.1016/j.jhydrol.2015.12.064","title":"An at-site flood estimation method in the context of nonstationarity II. Statistical analysis of floods in Quebec","year":2016,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Ministry of Higher Education; McGill University","keywords":"Statistics; Deviance (statistics); Flood myth; Statistic; Nonparametric statistics; Mathematics; Standard deviation; Context (archaeology); Series (stratigraphy); Gumbel distribution; Econometrics; Geography; Extreme value theory; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0009913633,0.000201622,0.0002720545,0.0006774383,0.0005308697,0.0005665686,0.0008952671,0.0002936297,0.002236218],"category_scores_gemma":[0.003090788,0.0001746855,0.000148804,0.0008389278,0.0001858855,0.0003801831,0.0003036229,0.0003684173,0.0002256684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002342433,"about_ca_system_score_gemma":0.003447301,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7215701,"about_ca_topic_score_gemma":0.8146375,"domain_scores_codex":[0.9997198,0.0001342763,0.000009669559,0.00005249676,0.0000438723,0.00003990122],"domain_scores_gemma":[0.9986721,0.0005918597,0.00006884297,0.0000806394,0.000535482,0.00005104206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002845162,0.0001524907,0.08084031,0.00008923625,0.0001806556,0.0002164789,0.0003624686,0.3171908,0.01022225,0.01110567,0.009323193,0.570032],"study_design_scores_gemma":[0.00001353653,0.00002140713,0.03489636,0.000008478282,0.00002451367,0.00002619717,0.00007210652,0.9604146,0.001009852,0.001046751,0.002449784,0.00001647557],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.400368,0.0004332174,0.5913279,0.0004344026,0.00004797298,0.0001382647,0.002186891,0.001042142,0.004021341],"genre_scores_gemma":[0.8586426,0.0001231437,0.1352181,0.00003474331,0.00002436693,0.00006862827,0.001196589,0.00009093738,0.004600876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2784299,"threshold_uncertainty_score":0.5601393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008392602145130646,"score_gpt":0.296762794572521,"score_spread":0.2883701924273904,"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."}}