{"id":"W2398526858","doi":"10.1016/j.eswa.2016.05.027","title":"A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":116,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Adaptive neuro fuzzy inference system; Artificial neural network; Data mining; Machine learning; Artificial intelligence; Neuro-fuzzy; Fuzzy logic; Selection (genetic algorithm); Parametric statistics; Supplier evaluation; Perceptron; Process (computing); Sensitivity (control systems); Supply chain management; Supply chain; Fuzzy control system; 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":[],"consensus_categories":[],"category_scores_codex":[0.001927225,0.0002201254,0.0003003077,0.0003240277,0.0003720622,0.0002846591,0.0002811717,0.00007233339,0.00002819774],"category_scores_gemma":[0.0004381099,0.000116834,0.00004397078,0.0004120748,0.0001016659,0.000526005,0.00004499575,0.00005478431,0.00003415581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002161141,"about_ca_system_score_gemma":0.0001555584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001851379,"about_ca_topic_score_gemma":0.00001361519,"domain_scores_codex":[0.9965774,0.000150006,0.0006763251,0.0008920602,0.001452352,0.0002517915],"domain_scores_gemma":[0.9954997,0.001518205,0.0004196906,0.0005484774,0.001865802,0.0001480802],"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":[0.001432107,0.0002283714,0.001271186,0.00002285401,0.000105097,3.247765e-7,0.002205909,0.8283973,0.009473287,0.009557868,0.004476649,0.142829],"study_design_scores_gemma":[0.001497938,0.0001464045,0.0002833679,0.00006242849,0.00002953299,0.00002379427,0.000508143,0.9791583,0.0004849003,0.005071294,0.01252622,0.0002076359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01221805,0.0001731272,0.9815072,0.0002322977,0.00005425937,0.005059256,0.00008212372,0.00008834483,0.0005852944],"genre_scores_gemma":[0.9584019,0.000007099063,0.03148456,0.0000472159,0.0001855273,0.009134118,0.00001648516,0.00003473689,0.0006883289],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9500227,"threshold_uncertainty_score":0.4764353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1372853844337297,"score_gpt":0.4290438737337837,"score_spread":0.291758489300054,"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."}}