{"id":"W3030411818","doi":"10.5267/j.dsl.2020.3.003","title":"Multi-objective optimization of selected non-traditional machining processes using NSGA-II","year":2020,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machining; Multi-objective optimization; Mathematical optimization; Manufacturing engineering; Computer science; Engineering; Mechanical engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001707158,0.0001222669,0.0001393517,0.000208373,0.000235555,0.0000739803,0.0002699167,0.0000354652,0.00004148103],"category_scores_gemma":[0.0004305539,0.0001168016,0.00002214011,0.00172053,0.0001058266,0.0006732929,0.0000486115,0.0001076113,0.000002313008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005528187,"about_ca_system_score_gemma":0.00008239573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003951558,"about_ca_topic_score_gemma":5.852391e-7,"domain_scores_codex":[0.9987854,0.000007698744,0.0002585204,0.0002747235,0.0004776579,0.0001959629],"domain_scores_gemma":[0.9994415,0.00007748415,0.00007833736,0.00009219413,0.0002191976,0.00009129216],"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":[0.000008816485,0.000009702915,0.0002354588,0.00003991132,0.000003851077,0.000001042159,0.0009800943,0.9564887,0.04155951,0.000001801988,0.0000593841,0.0006117456],"study_design_scores_gemma":[0.0002396927,0.00002240893,0.002064278,0.00005570177,0.000006195842,0.00000297356,0.00003940974,0.9494907,0.04793199,0.000006438995,0.00001121078,0.0001289674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3730096,0.00001198206,0.6266075,0.00009820934,0.00009038484,0.00007664022,0.000005460154,0.00006907232,0.00003116851],"genre_scores_gemma":[0.7624947,0.000007538069,0.2371572,0.0002781735,0.00003798429,0.00000332652,0.000007262883,0.00001313269,5.816116e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3894852,"threshold_uncertainty_score":0.4763031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03345535730387465,"score_gpt":0.253015751317962,"score_spread":0.2195603940140873,"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."}}