{"id":"W2239421946","doi":"","title":"The Comparison of GEV, Log-Pearson Type 3 and Gumbel Distributions in the Upper Thames River Watershed under Global Climate Models","year":2011,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gumbel distribution; Watershed; Hydrology (agriculture); Environmental science; Type (biology); Physical geography; Geography; Climatology; Mathematics; Statistics; Extreme value theory; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004150334,0.0001799258,0.0001966439,0.00004504853,0.0004659928,0.00004544969,0.000611742,0.00009746411,0.00002060564],"category_scores_gemma":[0.00001028245,0.0001216165,0.00004972104,0.0003431218,0.0008013731,0.0006830061,0.0006967222,0.0002384275,0.00008471417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026692,"about_ca_system_score_gemma":0.000006023407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007882556,"about_ca_topic_score_gemma":0.003039219,"domain_scores_codex":[0.9986031,0.0002794321,0.0001896146,0.0003101584,0.0002114278,0.0004062788],"domain_scores_gemma":[0.999429,0.00006205607,0.0001042585,0.0003329573,0.00001550287,0.00005619375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001160585,0.0001084568,0.9955102,0.000006931937,0.00004179692,0.00001805182,0.002495811,0.0001595095,0.00002571199,0.001404256,0.000005564293,0.0001076539],"study_design_scores_gemma":[0.0004120786,0.0001006573,0.9936088,0.00001605067,0.00007212749,0.000004319784,0.00169684,0.00001167912,0.000128376,0.003483267,0.0003220473,0.0001437258],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997094,0.00008237646,0.0003349743,0.0005119102,0.00007648853,0.0002257964,0.00001841552,0.00002489555,0.001631132],"genre_scores_gemma":[0.9993789,0.0001752131,0.00002836909,0.0001564774,0.000006321645,0.000001290528,0.000008736167,0.000006562494,0.0002381319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002960393,"threshold_uncertainty_score":0.4959378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1031964377502645,"score_gpt":0.3027691767276843,"score_spread":0.1995727389774198,"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."}}