{"id":"W1502470484","doi":"","title":"Deriving reservoir operating rules via fuzzy regression and ANFIS.","year":2003,"lang":"en","type":"article","venue":"European Society for Fuzzy Logic and Technology Conference","topic":"Water resources management and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adaptive neuro fuzzy inference system; Computer science; Fuzzy logic; Reservoir computing; Neuro-fuzzy; Mathematical optimization; Term (time); Fuzzy control system; Data mining; Artificial intelligence; Machine learning; Mathematics; Artificial neural network","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":[0.0009117498,0.0007250437,0.0006266939,0.0005917588,0.0003269709,0.0007517014,0.0006030098,0.0007742521,0.00153908],"category_scores_gemma":[0.003531893,0.0004531397,0.0006386702,0.000462625,0.0003186256,0.0008224162,0.0003732565,0.0008707233,0.0003314792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004903084,"about_ca_system_score_gemma":0.0008274753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008983263,"about_ca_topic_score_gemma":0.009014097,"domain_scores_codex":[0.9996891,0.00007659094,0.00003895851,0.00005004223,0.0001304565,0.00001479353],"domain_scores_gemma":[0.9992101,0.0005037109,0.0001116951,0.00003423593,0.0001301442,0.000009981636],"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":[0.00003140877,0.00002054066,0.0004420053,0.0001568579,0.00004016982,0.000080046,0.00007305021,0.9401786,0.003461734,0.00515855,0.0004007139,0.04995636],"study_design_scores_gemma":[0.000003520177,0.000009841521,0.0001079158,0.00001321177,0.000007007602,0.00001423098,0.000007540117,0.9965119,0.001151312,0.001684863,0.0004841694,0.000004539432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02369983,0.0003363744,0.970382,0.0001457235,0.00004960538,0.0001157128,0.0001597709,0.0005573712,0.004553613],"genre_scores_gemma":[0.5702455,0.0005525229,0.4249689,0.00005983985,0.0000311404,0.0003900874,0.0004019945,0.000079722,0.003270326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008983263,"threshold_uncertainty_score":0.0178619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744975004905162,"score_gpt":0.2093424587656416,"score_spread":0.19189270871659,"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."}}