{"id":"W3042345065","doi":"10.2166/ws.2020.153","title":"Multivariate drought risk analysis based on copula functions: a case study","year":2020,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Multivariate statistics; Return period; Copula (linguistics); Streamflow; Joint probability distribution; Index (typography); Statistics; Environmental science; Climatology; Geography; Physical geography; Mathematics; Econometrics; Drainage basin; Flood myth; Computer science; Cartography; Geology","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":[0.003145326,0.0006654419,0.0007189544,0.001586427,0.0006379411,0.001107453,0.0006739801,0.0008830156,0.001104566],"category_scores_gemma":[0.004862851,0.000307731,0.0009363078,0.001900604,0.0005318431,0.0008360716,0.0008691628,0.0008767663,0.00009604872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099053,"about_ca_system_score_gemma":0.0005259981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01143242,"about_ca_topic_score_gemma":0.00731818,"domain_scores_codex":[0.9990571,0.0005821934,0.00003747149,0.00007780499,0.0001354244,0.0001099039],"domain_scores_gemma":[0.9959576,0.003166252,0.0002760036,0.0001699456,0.0002910935,0.0001391022],"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":[0.0003559686,0.0006469555,0.08477353,0.0001711664,0.0003770334,0.008804861,0.0007226474,0.8411109,0.001302655,0.01673567,0.003306901,0.04169174],"study_design_scores_gemma":[0.00001912641,0.0000857058,0.01109661,0.00001190917,0.00004051468,0.0003365982,0.0003342761,0.9845487,0.0003104759,0.002665727,0.0005238816,0.00002642913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9384931,0.0005563808,0.0573506,0.000500707,0.00001820132,0.00009052992,0.0003495119,0.00008720994,0.002553755],"genre_scores_gemma":[0.9910363,0.0002265674,0.008088494,0.00001093652,0.00001485302,0.00003099878,0.000104009,0.000009969051,0.0004778455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01143242,"threshold_uncertainty_score":0.02273172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00906837586425285,"score_gpt":0.2317530757553486,"score_spread":0.2226846998910957,"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."}}