{"id":"W4292566194","doi":"10.5267/j.msl.2022.5.001","title":"An integrated approach of VIKOR and teaching learning based optimization algorithm for milling machinability computations","year":2022,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machinability; Materials science; Machining; Response surface methodology; Computer science; Process (computing); Productivity; Composite number; Algorithm; Process engineering; Mechanical engineering; Composite material; Metallurgy; Machine learning; Engineering","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":[0.0006928761,0.0007970641,0.0009254859,0.0004844347,0.0003906746,0.0005995702,0.0009679372,0.001129425,0.002663608],"category_scores_gemma":[0.001496427,0.000441417,0.0006345366,0.0004188769,0.0004112477,0.0004659509,0.000695482,0.0009798966,0.0004615689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004398484,"about_ca_system_score_gemma":0.001135713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005979023,"about_ca_topic_score_gemma":0.005094709,"domain_scores_codex":[0.9997504,0.00007072584,0.00001923157,0.00004511013,0.00007785097,0.00003669047],"domain_scores_gemma":[0.9995746,0.0002630576,0.00003299108,0.00001772614,0.0000954901,0.00001616282],"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":[0.00005699589,0.00006857599,0.00064275,0.0001068998,0.00003775399,0.0000446838,0.00006331238,0.9010656,0.002291962,0.004621928,0.0005702511,0.09042935],"study_design_scores_gemma":[0.000004094856,0.00001712857,0.00004346956,0.000003215702,0.000002013913,0.000003382681,0.000004132818,0.9990239,0.0002183157,0.0004208645,0.0002577768,0.00000165897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01136095,0.0001761342,0.9856818,0.00009102495,0.00003099799,0.00005003015,0.00001437485,0.0003728625,0.002221938],"genre_scores_gemma":[0.3097653,0.0002226448,0.6847629,0.0001298111,0.00004060379,0.0005000791,0.0001209737,0.0001486652,0.004308937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005979023,"threshold_uncertainty_score":0.01188844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006247979231798836,"score_gpt":0.2325610296508677,"score_spread":0.2263130504190689,"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."}}