{"id":"W1559665021","doi":"10.1111/j.1753-318x.2008.00022.x","title":"Bivariate flood frequency analysis: Part 1. Determination of marginals by parametric and nonparametric techniques","year":2008,"lang":"en","type":"article","venue":"Journal of Flood Risk Management","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Nonparametric statistics; Marginal distribution; Flood myth; Bivariate analysis; Parametric statistics; Mathematics; Statistics; Random variable; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001206827,0.0001720218,0.0005063631,0.001154317,0.0001434255,0.00001819606,0.0003031211,0.00009672927,0.0004854467],"category_scores_gemma":[0.00009447933,0.000140651,0.0002636162,0.003300528,0.0001711736,0.0003314704,0.0001193189,0.000205664,0.0000245963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008405821,"about_ca_system_score_gemma":0.000006353058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002128462,"about_ca_topic_score_gemma":0.00002682508,"domain_scores_codex":[0.9979638,0.0002103883,0.0007800734,0.0002511498,0.0005666044,0.0002280054],"domain_scores_gemma":[0.9984753,0.00009959617,0.001004054,0.0002696052,0.00003967655,0.0001118125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007439296,0.0008968225,0.9271387,0.00003940198,0.00268765,0.0002442252,0.0002308754,0.001530688,0.0003557906,0.00009189293,0.004155099,0.06255444],"study_design_scores_gemma":[0.002476202,0.001937561,0.936255,0.00006381348,0.01911002,0.0001998743,0.0002021035,0.01002026,0.01040506,0.008814353,0.009584151,0.0009316253],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623907,0.001291089,0.03087996,0.0001224478,0.00006254727,0.0001902962,0.00001094256,0.0000237466,0.005028312],"genre_scores_gemma":[0.9614672,0.00819924,0.03002857,0.00005563219,0.00002356856,0.000006934682,0.000002882074,0.00001054393,0.0002054424],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06162282,"threshold_uncertainty_score":0.5735582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006507473549171311,"score_gpt":0.2225826863014989,"score_spread":0.2160752127523276,"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."}}