{"id":"W4311858100","doi":"10.3390/hydrology9120221","title":"Trivariate Joint Distribution Modelling of Compound Events Using the Nonparametric D-Vine Copula Developed Based on a Bernstein and Beta Kernel Copula Density Framework","year":2022,"lang":"en","type":"article","venue":"Hydrology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Vine copula; Copula (linguistics); Nonparametric statistics; Kernel density estimation; Mathematics; Joint probability distribution; Econometrics; Statistics; Estimator; Marginal distribution; Parametric statistics; Univariate; Random variable; Multivariate statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0009829277,0.000168931,0.0003930836,0.00008879026,0.0005957553,0.000007792553,0.0002457465,0.0001409458,0.0004220271],"category_scores_gemma":[0.00008057783,0.000146251,0.00009556372,0.0007596233,0.0002707322,0.00004879811,0.0003682063,0.0004427487,0.00001650735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001990695,"about_ca_system_score_gemma":0.00002404992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001328223,"about_ca_topic_score_gemma":0.00009872949,"domain_scores_codex":[0.9979421,0.0005947427,0.0003862616,0.000417259,0.0003385071,0.0003210971],"domain_scores_gemma":[0.9989945,0.0002983809,0.0002633031,0.0003639581,0.00001132494,0.00006857027],"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":[0.0002096123,0.0001996264,0.09506142,0.000004487803,0.00004717,0.00001792407,0.0001510374,0.9033507,0.0004779679,0.0002978119,0.00002805959,0.0001541896],"study_design_scores_gemma":[0.0005338633,0.0002036351,0.03122977,0.000005556521,0.0001439367,0.00003117025,0.00001480864,0.9632887,0.0002038509,0.003935817,0.0002574295,0.0001514896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8665673,0.00004214473,0.1324967,0.0004872706,0.00008985517,0.0001864717,0.00003124874,0.00001474313,0.00008427255],"genre_scores_gemma":[0.996738,0.000007518334,0.002528785,0.00057649,0.00001647814,0.00001517807,0.0000823993,0.00001162064,0.00002358395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1301707,"threshold_uncertainty_score":0.5963942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03659844109157693,"score_gpt":0.2520943941845985,"score_spread":0.2154959530930216,"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."}}