{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002100879,0.00008052499,0.0004689858,0.0003424521,0.00003816731,0.000002306911,0.0002400868,0.0001070719,0.001317625],"category_scores_gemma":[0.0002420852,0.00004728363,0.0001145717,0.0006342969,0.0003082195,0.0002212054,0.00004795045,0.0001381885,0.000009183172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009995672,"about_ca_system_score_gemma":0.00002745621,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006673159,"about_ca_topic_score_gemma":0.1228486,"domain_scores_codex":[0.9978552,0.0008841212,0.0007241704,0.0001374006,0.0002494619,0.0001496173],"domain_scores_gemma":[0.9984925,0.0007851004,0.0004738597,0.0001823912,0.00002323412,0.00004286564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003488002,0.0005200288,0.8825141,0.000003269295,0.0002907994,0.00004822087,0.003961417,0.08575639,0.01395324,0.001057433,0.0001022048,0.01144416],"study_design_scores_gemma":[0.001040812,0.0005135913,0.8417063,0.000006059052,0.0006782833,0.00003311753,0.0001417407,0.151601,0.0009700533,0.00312955,0.0001031168,0.00007636016],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9531075,0.0000215467,0.04534515,0.00124804,0.00002056287,0.00004369752,0.00002080248,0.00000109663,0.0001916071],"genre_scores_gemma":[0.9915289,0.00001044639,0.00821717,0.000194625,0.00000786804,0.000001764612,0.000007551173,0.000002701565,0.00002895145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1161755,"threshold_uncertainty_score":0.9999415,"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."}}