{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005070709,0.0005259624,0.000650926,0.002264045,0.0003654046,0.0009666572,0.0005438926,0.0003460467,0.001695898],"category_scores_gemma":[0.02031009,0.0005016489,0.0008926215,0.001685059,0.001055735,0.001300503,0.001098411,0.0009265423,0.0004039746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002613203,"about_ca_system_score_gemma":0.0006055731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001643629,"about_ca_topic_score_gemma":0.001064356,"domain_scores_codex":[0.9985625,0.000874405,0.00006225235,0.0001680049,0.000266995,0.00006580271],"domain_scores_gemma":[0.9921625,0.00630917,0.0004723399,0.0005964962,0.0003847732,0.00007470146],"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":[0.0001394482,0.0001169899,0.05001545,0.0002563577,0.000203458,0.0002458655,0.0006147574,0.150284,0.01009558,0.07751752,0.001412856,0.7090977],"study_design_scores_gemma":[0.000009692056,0.00008273054,0.04865784,0.00005150454,0.00005383703,0.0003804263,0.000214482,0.8845974,0.003954849,0.05851972,0.003408037,0.00006944833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02109086,0.0001370346,0.9779659,0.00002740847,0.000004198537,0.00002426464,0.00004687925,0.0001173711,0.0005861251],"genre_scores_gemma":[0.6811755,0.0004884587,0.3168008,0.00001452884,0.00004773837,0.0002258939,0.0001934981,0.000104779,0.0009487559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005070709,"threshold_uncertainty_score":0.02681684,"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."}}