{"id":"W2618162153","doi":"10.1016/j.eswa.2017.05.057","title":"Bi-level plant selection and production allocation model under type-2 fuzzy demand","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Mathematical optimization; Selection (genetic algorithm); Production (economics); Fuzzy logic; Parametric statistics; Decision maker; Degree (music); Computer science; Type (biology); Fuzzy number; Parametric programming; Sensitivity (control systems); Order (exchange); Mathematics; Operations research; Fuzzy set; Artificial intelligence; Statistics; Economics; 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":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001288243,0.0001953732,0.0002723971,0.0002517331,0.001393758,0.001052003,0.0005742603,0.000115441,0.00001221626],"category_scores_gemma":[0.0006004172,0.0001397814,0.00002930215,0.0003233814,0.000115176,0.0007180601,0.0001076934,0.0001142268,0.0001233839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008221164,"about_ca_system_score_gemma":0.0001103154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002571357,"about_ca_topic_score_gemma":0.0002922467,"domain_scores_codex":[0.9973683,0.0001000495,0.000566397,0.0008305048,0.0009099655,0.0002247404],"domain_scores_gemma":[0.9969617,0.0002303248,0.0005730891,0.001377745,0.0007196718,0.0001375267],"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":[0.0009818105,0.001022976,0.02581176,0.0001565489,0.0003575181,0.000006891264,0.009137874,0.2576714,0.2459391,0.2206799,0.1228251,0.1154091],"study_design_scores_gemma":[0.001116645,0.0001087428,0.02781347,0.000194293,0.00004118726,0.0003504944,0.002184524,0.8838729,0.002190332,0.01791717,0.06337852,0.000831681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0589142,0.0004875186,0.9342507,0.00223935,0.0005134896,0.001663475,0.00002874173,0.0001265344,0.001775992],"genre_scores_gemma":[0.9856156,0.00004220093,0.01008103,0.00006485172,0.0003635996,0.0007045439,0.00001502384,0.00002665141,0.003086562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9267013,"threshold_uncertainty_score":0.999985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2445522141893345,"score_gpt":0.4184144982401229,"score_spread":0.1738622840507884,"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."}}