{"id":"W3101221099","doi":"10.1155/2020/8763631","title":"Evaluating the Dependence between Temperature and Precipitation to Better Estimate the Risks of Concurrent Extreme Weather Events","year":2020,"lang":"en","type":"article","venue":"Advances in Meteorology","topic":"Climate variability and models","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Resources Canada; Environment and Climate Change Canada","keywords":"Copula (linguistics); Extreme weather; Climatology; Precipitation; Environmental science; Extreme value theory; Gumbel distribution; Tail dependence; Climate change; Climate extremes; Geography; Econometrics; Meteorology; Mathematics; Multivariate statistics; Statistics; Ecology; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007793928,0.00008685012,0.0001423579,0.000009601215,0.0000733746,0.000006196104,0.0002345243,0.00004675752,0.0001390238],"category_scores_gemma":[0.0003488898,0.0000506326,0.00001955669,0.0001366064,0.0001695901,0.0001606292,0.0001981978,0.0001644967,0.00001493674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002042664,"about_ca_system_score_gemma":0.000004449676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005068706,"about_ca_topic_score_gemma":0.0001391093,"domain_scores_codex":[0.9988919,0.000302874,0.000213869,0.0002583035,0.0001741638,0.0001588356],"domain_scores_gemma":[0.9991748,0.0005482851,0.00008150974,0.0001509076,0.000007756209,0.00003668714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007274098,0.00002647245,0.7300546,0.00002622145,0.00001307476,7.207678e-7,0.006126291,0.08810405,0.03151169,0.0002589312,0.00002190662,0.1437834],"study_design_scores_gemma":[0.001131785,0.00140383,0.8941503,0.00005841021,0.0001236733,0.000005445126,0.00052602,0.06012143,0.003834371,0.0356309,0.002646707,0.0003671501],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930214,0.0003332341,0.001480226,0.004574758,0.00007037289,0.0003687616,0.00001063021,0.000006702513,0.0001339019],"genre_scores_gemma":[0.9954585,0.00006805344,0.00346938,0.0009227316,0.00002432865,0.00004510583,0.000002258523,0.000005094812,0.000004537354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1640957,"threshold_uncertainty_score":0.2064737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1140959575111651,"score_gpt":0.4079775382812789,"score_spread":0.2938815807701138,"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."}}