{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001109863,0.0003731633,0.0005078536,0.001193025,0.001411512,0.0001091455,0.001182276,0.0002385653,0.003254395],"category_scores_gemma":[0.0000824314,0.0002216625,0.0002141199,0.005602611,0.001841551,0.0005019986,0.0008191171,0.0005674977,0.004099254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001489688,"about_ca_system_score_gemma":0.00001897165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002308676,"about_ca_topic_score_gemma":0.001011791,"domain_scores_codex":[0.996051,0.0002048412,0.0004838608,0.001584971,0.0006183758,0.001057022],"domain_scores_gemma":[0.9984432,0.00003010232,0.00008979667,0.001123697,0.00003681322,0.0002764027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006818047,0.0004986468,0.9344375,0.000001807137,0.0001843131,0.002345128,0.004820108,0.04281396,0.01364366,0.000005343409,0.0001091009,0.001072216],"study_design_scores_gemma":[0.003810651,0.003467144,0.02219746,0.00000650216,0.00415166,0.0004124153,0.005952942,0.6938912,0.25584,0.0009223954,0.007491271,0.001856448],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846082,0.000003923587,0.007039563,0.006897179,0.0001082103,0.000431559,0.0000141968,0.0003896581,0.0005075262],"genre_scores_gemma":[0.9974425,9.333959e-7,0.001047076,0.001030503,0.00002519199,0.0001232867,0.00001966804,0.00001794809,0.0002929071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9122401,"threshold_uncertainty_score":0.9998885,"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."}}