{"id":"W4401395101","doi":"10.1007/s12665-024-11743-7","title":"Joint probabilistic assessments of four-dimensional flood characteristics using the vine copula-based methodology","year":2024,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Universiti Teknologi Malaysia","keywords":"Vine copula; Copula (linguistics); Bivariate analysis; Joint probability distribution; Univariate; Gumbel distribution; Econometrics; Flood myth; Tail dependence; Return period; Statistics; Marginal distribution; Parametric statistics; Probabilistic logic; Mathematics; Multivariate statistics; Computer science; Random variable; Extreme value theory; 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":[0.002648682,0.0007242578,0.0008264368,0.00154918,0.0003450607,0.001164734,0.0007011807,0.0005846502,0.001163564],"category_scores_gemma":[0.005993062,0.0004975527,0.001031541,0.001197876,0.0003036032,0.001669079,0.00119021,0.0005039664,0.0001807406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005371453,"about_ca_system_score_gemma":0.0008087923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006400551,"about_ca_topic_score_gemma":0.007354723,"domain_scores_codex":[0.9990543,0.0004694435,0.00004355002,0.0001606586,0.0001951039,0.00007693808],"domain_scores_gemma":[0.9978993,0.001208684,0.0002939384,0.0001863183,0.0003328168,0.00007884627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000367202,0.00001983356,0.002988356,0.00001455717,0.0001043263,0.00003410669,0.00002688842,0.972146,0.0008868633,0.00277651,0.0001647829,0.0208011],"study_design_scores_gemma":[0.000001471075,0.00001100365,0.0008880421,0.000002150704,0.000006618308,0.000008795922,0.000004653205,0.9974606,0.0002171268,0.001306599,0.00008620174,0.000006621124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1280424,0.0001361403,0.8693756,0.00006275686,0.0000116046,0.00005657188,0.0002922112,0.0002671636,0.001755507],"genre_scores_gemma":[0.9118254,0.0001207213,0.08610503,0.00001831855,0.00001362505,0.00008207518,0.0004592939,0.00006441872,0.001311218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006400551,"threshold_uncertainty_score":0.01400775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08083117949035733,"score_gpt":0.3120126926815918,"score_spread":0.2311815131912344,"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."}}