{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002743977,0.0003836947,0.0003082781,0.00113579,0.0003278733,0.001508064,0.0005435463,0.0004703583,0.001002047],"category_scores_gemma":[0.007404955,0.0001563036,0.0006697323,0.001362336,0.0006257855,0.0009608063,0.000496314,0.0005161761,0.00007751895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001261596,"about_ca_system_score_gemma":0.0008055811,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05312017,"about_ca_topic_score_gemma":0.0317985,"domain_scores_codex":[0.9994399,0.00028656,0.00002227327,0.00008645455,0.0000791657,0.00008569851],"domain_scores_gemma":[0.9964237,0.002774646,0.0002510076,0.0001656559,0.0002696439,0.0001153356],"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":[0.000242742,0.00005429328,0.09848386,0.00004004336,0.0001054489,0.0004155918,0.000330957,0.8799721,0.0006870849,0.009423018,0.001428399,0.008816381],"study_design_scores_gemma":[0.00001483425,0.00004661184,0.06271086,0.00001689899,0.00002447367,0.00006448053,0.0003820749,0.9327372,0.0002902322,0.003081617,0.0005963795,0.00003426717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986613,0.00008591195,0.01061721,0.0001374812,0.000009701535,0.00001597053,0.0004934238,0.0001371413,0.001890323],"genre_scores_gemma":[0.9969341,0.00006931469,0.002110748,0.00001278402,0.000004694947,0.00001246396,0.0005678728,0.00001740194,0.0002706623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9468798,"threshold_uncertainty_score":0.1056219,"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."}}