{"id":"W1575463142","doi":"10.1029/2011wr011040","title":"Exploratory functional flood frequency analysis and outlier detection","year":2012,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique","funders":"","keywords":"Hydrograph; Flood myth; Context (archaeology); Computer science; Outlier; Univariate; Functional data analysis; Data mining; Hydrology (agriculture); Multivariate statistics; Environmental science; Geography; Geology; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"category_scores_codex":[0.001776756,0.00009964668,0.0001436511,0.0003323458,0.0004954645,0.00004555023,0.0001369821,0.0001117479,0.005869011],"category_scores_gemma":[0.00003382825,0.00006853347,0.00008307123,0.0008487147,0.0004008203,0.0003612217,0.0002752759,0.0003071193,0.002647096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007987067,"about_ca_system_score_gemma":0.000001864013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006463881,"about_ca_topic_score_gemma":0.0007739941,"domain_scores_codex":[0.9979328,0.0004209222,0.0001518428,0.0003040112,0.0005854417,0.0006050103],"domain_scores_gemma":[0.9994348,0.00005690024,0.00001719049,0.0002602848,0.00001735531,0.0002134112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003638928,0.00008657898,0.9557838,0.000003591061,0.0002585034,0.000004571524,0.005307814,0.0002570624,0.03577588,0.000006495717,0.0002660849,0.002213226],"study_design_scores_gemma":[0.0003981727,0.0001277518,0.8764428,0.000002254449,0.000386422,0.00001231705,0.0008440124,0.002036753,0.05514408,0.00116786,0.06308489,0.0003526496],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928811,0.0001511466,0.0006692628,0.0001804127,0.00003606579,0.00007008974,0.000001528201,0.00004029906,0.005970147],"genre_scores_gemma":[0.9978058,0.00001365885,0.00008194603,0.00005525166,0.0001407864,0.00003861932,0.000009127099,0.00001239577,0.001842409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07934096,"threshold_uncertainty_score":0.9981295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03607489950290302,"score_gpt":0.2780189450910857,"score_spread":0.2419440455881827,"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."}}