{"id":"W1949379220","doi":"10.1109/nafips.2005.1548642","title":"Hydrologic model Calibration using Fuzzy TSK surrogate model","year":2005,"lang":"en","type":"article","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Surrogate model; Robustness (evolution); Mathematical optimization; Curse of dimensionality; Computer science; Calibration; Fuzzy logic; Minification; Function (biology); Algorithm; Mathematics; Artificial intelligence; Statistics","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":[0.001064174,0.0004363593,0.0007195782,0.0004556513,0.0004549914,0.001027513,0.0006608753,0.0009892513,0.001510244],"category_scores_gemma":[0.003581231,0.0002895741,0.0006182283,0.0009119053,0.0004735877,0.001017717,0.0005322151,0.00100143,0.0003880444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007874881,"about_ca_system_score_gemma":0.001206908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00475093,"about_ca_topic_score_gemma":0.002624412,"domain_scores_codex":[0.9995415,0.0001639187,0.00003830543,0.00006699937,0.0001543148,0.00003507194],"domain_scores_gemma":[0.9990368,0.0003762232,0.00009040895,0.0001381546,0.0003315545,0.00002687491],"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":[0.00002188476,0.000009713646,0.000328055,0.00001504542,0.000006444314,0.00001379211,0.00001370127,0.9915218,0.001222957,0.001450896,0.0001276,0.005268112],"study_design_scores_gemma":[0.000002262796,0.000006191839,0.0000946497,0.000001970864,0.000001138668,0.000003957738,0.000004707902,0.9982312,0.0008365039,0.0006457969,0.0001685117,0.000003113378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1327735,0.0001042596,0.8597894,0.0002325339,0.00004059428,0.00007601715,0.0003475133,0.0007475619,0.005888604],"genre_scores_gemma":[0.8792953,0.0001372761,0.1183664,0.00003184198,0.00000780794,0.0001309694,0.0004830929,0.00007605689,0.001471267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00475093,"threshold_uncertainty_score":0.009446561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05084249324267324,"score_gpt":0.2624028037299463,"score_spread":0.2115603104872731,"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."}}