{"id":"W2591334185","doi":"10.1007/s10845-017-1307-5","title":"A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":110,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Fuzzy logic; Adaptive neuro fuzzy inference system; Computer science; Fuzzy set; Measure (data warehouse); Inference; Set (abstract data type); Industrial engineering; Fuzzy control system; Data mining; Artificial intelligence; Machine learning; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000426737,0.0003311345,0.0007169221,0.0002654685,0.0002933576,0.0002151559,0.0004501284,0.00007107082,0.000002470324],"category_scores_gemma":[0.00002932557,0.0002573981,0.00006208111,0.00002580254,0.00009855941,0.0006119722,0.0001013477,0.0004270189,0.000002373193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001755912,"about_ca_system_score_gemma":0.00002317396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003624593,"about_ca_topic_score_gemma":0.00001255008,"domain_scores_codex":[0.9982179,0.00004113685,0.0008017957,0.0002000256,0.0004441328,0.0002949598],"domain_scores_gemma":[0.9982781,0.0001169278,0.0008604479,0.0004424791,0.0001616442,0.0001404184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001411746,0.00005117811,0.002109125,0.0008324994,0.0002741599,0.00004877891,0.001257038,0.9925948,0.0001406521,0.00003122313,0.000003000046,0.002516308],"study_design_scores_gemma":[0.001254733,0.000972651,0.03005762,0.002364045,0.0002418352,0.000363431,0.007171537,0.05612333,0.9006954,0.00003418501,0.00008286813,0.0006383857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9605097,0.0002701932,0.03721901,0.00000361902,0.0005013769,0.0003334395,0.000003304504,0.00006568362,0.001093699],"genre_scores_gemma":[0.9975759,0.0002333541,0.001994075,9.87959e-7,0.0001195858,0.000007743451,6.758555e-7,0.00003698147,0.00003064725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9364715,"threshold_uncertainty_score":0.9999878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03769556660885192,"score_gpt":0.260228341134721,"score_spread":0.2225327745258691,"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."}}