{"id":"W2061754679","doi":"10.1002/hyp.259","title":"A bivariate gamma distribution for use in multivariate flood frequency analysis","year":2001,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":188,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Bivariate analysis; Joint probability distribution; Flood myth; Multivariate statistics; Marginal distribution; Multivariate analysis; Statistics; Gamma distribution; 100-year flood; Return period; Distribution (mathematics); Hydrology (agriculture); Environmental science; Multivariate normal distribution; Mathematics; Geography; Geology; Random variable; Geotechnical engineering","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":[],"category_scores_codex":[0.0004270616,0.0002113171,0.0003982237,0.0001005471,0.0001754326,0.00004473772,0.0002777621,0.0002466263,0.001275469],"category_scores_gemma":[0.0009186019,0.0001629193,0.0001741655,0.002532092,0.0002046266,0.0004396782,0.00009898102,0.0001673896,0.0001903582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008273611,"about_ca_system_score_gemma":0.00001377881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001309586,"about_ca_topic_score_gemma":0.004171647,"domain_scores_codex":[0.9981481,0.0001154205,0.000388314,0.0006442415,0.0001905164,0.0005133906],"domain_scores_gemma":[0.9991366,0.0003490571,0.0001255256,0.0002493109,0.00002568683,0.0001138712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001869415,0.0004142601,0.9701515,0.0000111244,0.0001581802,0.00005658636,0.00008551028,0.02761862,0.0005581459,0.0001773388,0.00008260885,0.0004992096],"study_design_scores_gemma":[0.001556673,0.0003602869,0.8041707,0.000008463292,0.001054272,0.00001557242,0.00001829851,0.1574748,0.0004205258,0.02929081,0.005000179,0.0006294958],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9402317,0.00006224278,0.05819552,0.0004515703,0.00002337,0.0002463031,0.00004264012,0.00012188,0.0006248072],"genre_scores_gemma":[0.9971378,0.00005021553,0.001940772,0.0003301202,0.00003248769,0.0001408262,0.0001939993,0.00001047918,0.0001632447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1659808,"threshold_uncertainty_score":0.9996375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02693700887820406,"score_gpt":0.2629935128014266,"score_spread":0.2360565039232226,"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."}}