{"id":"W2112618766","doi":"10.1109/tfuzz.2008.925918","title":"An Evolving Fuzzy Predictor for Industrial Applications","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Cluster analysis; Benchmark (surveying); Computer science; Convergence (economics); Data mining; Fuzzy logic; Identification (biology); Constraint (computer-aided design); Machine learning; Nonlinear system; Artificial intelligence; Mathematics","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":[0.0006281024,0.0003431464,0.0004493737,0.0003631437,0.0003285573,0.0004653049,0.0006087657,0.0006205036,0.001956035],"category_scores_gemma":[0.002165938,0.0001760958,0.0002260696,0.0006214342,0.000283931,0.0006362173,0.0004246504,0.0009729569,0.0005151508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003328355,"about_ca_system_score_gemma":0.0006680873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003989016,"about_ca_topic_score_gemma":0.002803087,"domain_scores_codex":[0.9997843,0.00004629155,0.00001010932,0.00005539702,0.00008850764,0.00001535478],"domain_scores_gemma":[0.9994851,0.0002144353,0.0000416951,0.00004219532,0.0001963701,0.00002011918],"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":[0.00008594997,0.00003925949,0.001402901,0.00007260105,0.00002368074,0.00009080386,0.00007140305,0.672001,0.008080103,0.01410186,0.002031515,0.301999],"study_design_scores_gemma":[0.000001639773,0.00001373337,0.0001430666,0.000003161352,0.000002068204,0.000008292413,0.000002349983,0.9969215,0.0007613984,0.001361995,0.0007773422,0.000003334404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01256586,0.0003104217,0.9845665,0.0001027198,0.00005293638,0.00001637024,0.00003703507,0.0005395918,0.00180847],"genre_scores_gemma":[0.6790285,0.0007468026,0.3131072,0.0001159554,0.000101834,0.0001051726,0.0002222369,0.00006627845,0.006506098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003989016,"threshold_uncertainty_score":0.00793159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04013031807665191,"score_gpt":0.2441551761958654,"score_spread":0.2040248581192135,"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."}}