{"id":"W4283158177","doi":"10.20944/preprints202206.0259.v1","title":"Parametric Vine Copula Framework in the Trivariate Probability Analysis of Compound Flooding Events","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; Institute for Catastrophic Loss Reduction","keywords":"Vine copula; Copula (linguistics); Multivariate statistics; Flooding (psychology); Tail dependence; Parametric statistics; Flood myth; Conditional independence; Storm surge; Storm; Mathematics; Statistics; Environmental science; Econometrics; Meteorology; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002245209,0.0007653948,0.0008551482,0.001237857,0.0003123173,0.001437436,0.001402542,0.000769657,0.001772248],"category_scores_gemma":[0.006974022,0.0004366621,0.00117753,0.001365235,0.001075241,0.001214522,0.0009762052,0.001355433,0.0003393108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007368732,"about_ca_system_score_gemma":0.0008667161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01039075,"about_ca_topic_score_gemma":0.004992272,"domain_scores_codex":[0.9989296,0.0005535479,0.00004202861,0.0001994431,0.0001575762,0.000117822],"domain_scores_gemma":[0.9978836,0.001336831,0.0002948502,0.0001665168,0.0002313727,0.00008671233],"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":[0.00002563742,0.00002543306,0.002276623,0.0000472204,0.0001305703,0.0002906321,0.0001154631,0.8971634,0.000861978,0.08393789,0.00060183,0.01452327],"study_design_scores_gemma":[0.000001468579,0.00001007408,0.0006852627,0.000004685688,0.000008854842,0.00002819398,0.00001535553,0.9881161,0.00007353986,0.01062553,0.0004231788,0.000007791733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02970183,0.0005184047,0.9671789,0.0001358832,0.00002535081,0.00003268847,0.0002049299,0.0001195951,0.00208232],"genre_scores_gemma":[0.8856913,0.001506221,0.1056999,0.00007681937,0.0001075534,0.0002282267,0.0006244197,0.0001378207,0.005927837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01039075,"threshold_uncertainty_score":0.02066058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1000707094385161,"score_gpt":0.349828832084742,"score_spread":0.2497581226462259,"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."}}