{"id":"W2739349198","doi":"10.5267/j.ijiec.2017.6.002","title":"Parameters optimization of fabric finishing system of a textile industry using teaching–learning-based optimization algorithm","year":2017,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Textile materials and evaluations","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Textile; Optimization algorithm; Textile industry; Computer science; System optimization; Algorithm; Engineering; Engineering drawing; Manufacturing engineering; Mathematical optimization; Mathematics; Materials science; Composite material","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000440347,0.0005591788,0.0006226752,0.0003459147,0.0003494844,0.0004356512,0.0005215212,0.0006755872,0.001060248],"category_scores_gemma":[0.0008009914,0.0002328886,0.0005384773,0.0003413465,0.0003486272,0.0004346098,0.0004011763,0.0004251188,0.0001283526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004940268,"about_ca_system_score_gemma":0.0008692332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004307747,"about_ca_topic_score_gemma":0.002737826,"domain_scores_codex":[0.9998295,0.00004976122,0.000008633485,0.0000336991,0.00005369041,0.00002481122],"domain_scores_gemma":[0.9997537,0.0001307118,0.00003480965,0.00001343566,0.00005674951,0.00001061706],"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":[0.00001670077,0.00002483833,0.0003324198,0.00002632644,0.00001331218,0.00001537568,0.00001543068,0.983489,0.001353683,0.0006725173,0.00009542132,0.01394505],"study_design_scores_gemma":[0.000004026279,0.00001333219,0.00006936749,0.000001188026,0.000002702783,0.000004124657,0.000002328017,0.999359,0.0003033742,0.0001563611,0.00008307825,0.000001246052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07167538,0.0002467221,0.9237085,0.00009881726,0.00002166793,0.00004767357,0.00001658536,0.0002152852,0.003969332],"genre_scores_gemma":[0.8616398,0.0002020564,0.1353844,0.00004013709,0.00001539815,0.0001596344,0.00005626808,0.0000414837,0.002460867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004307747,"threshold_uncertainty_score":0.008565366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04375558342938465,"score_gpt":0.2995344251059588,"score_spread":0.2557788416765742,"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."}}