{"id":"W1972813808","doi":"10.1016/j.fss.2006.10.024","title":"Inferring operating rules for reservoir operations using fuzzy regression and ANFIS","year":2006,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Water resources management and optimization","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adaptive neuro fuzzy inference system; Mathematical optimization; Fuzzy logic; Mathematics; Term (time); Computer science; Fuzzy control system; Artificial intelligence","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":[0.001072534,0.0006818498,0.000680685,0.000793486,0.0003234504,0.0008972578,0.0006350944,0.000828067,0.0009066538],"category_scores_gemma":[0.005231836,0.0004933597,0.0006005081,0.000435653,0.0003292892,0.0009343459,0.0002153765,0.0007576896,0.0001960159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000655611,"about_ca_system_score_gemma":0.0006427118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01275586,"about_ca_topic_score_gemma":0.01181993,"domain_scores_codex":[0.9996765,0.00007276518,0.00004337733,0.00007498706,0.00009702861,0.00003536295],"domain_scores_gemma":[0.9985946,0.001035902,0.0001392383,0.00005546181,0.0001570058,0.0000177418],"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":[0.00008729005,0.00004913771,0.002362437,0.00005448757,0.00004787974,0.00009599708,0.00006378443,0.9590743,0.003638878,0.001246464,0.0002112175,0.03306812],"study_design_scores_gemma":[0.000002371411,0.000004790678,0.0002586613,0.000003232641,0.000005817855,0.000005975352,0.000004756847,0.9982876,0.0007845125,0.0006075059,0.00003188637,0.000002838169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2765617,0.0002445179,0.7194149,0.000147294,0.00003425222,0.00009129848,0.0003013533,0.001196471,0.002008265],"genre_scores_gemma":[0.939427,0.0001066065,0.05979245,0.00001499851,0.000008833969,0.00005389845,0.0001394152,0.00002123792,0.000435568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01275586,"threshold_uncertainty_score":0.02536327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0252647195140925,"score_gpt":0.2416358731132616,"score_spread":0.2163711535991691,"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."}}