{"id":"W4249151868","doi":"10.1504/ijmtm.2020.107310","title":"Grouping and sequencing of machining operations for high-volume transfer lines","year":2020,"lang":"en","type":"article","venue":"International Journal of Manufacturing Technology and Management","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Machining; Volume (thermodynamics); Engineering; Engineering drawing; Manufacturing engineering; Computer science; Mechanical engineering; Physics","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":[0.00008964954,0.00008587552,0.0001388879,0.000290118,0.00003906645,0.00002514362,0.0001442167,0.00005340314,0.000008075134],"category_scores_gemma":[0.00001390463,0.00008143146,0.00002556291,0.00003762122,0.00003085371,0.0001535732,0.00004791713,0.0001056153,1.829108e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002048803,"about_ca_system_score_gemma":0.000004048245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002160954,"about_ca_topic_score_gemma":0.000001814676,"domain_scores_codex":[0.9994546,0.000003954639,0.0002790469,0.00008758928,0.00009676622,0.00007809213],"domain_scores_gemma":[0.9998209,0.00001449572,0.00004124809,0.00003820008,0.00005660594,0.00002856188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001421116,0.00002993951,0.0006286787,0.001249548,0.001142284,0.00005302554,0.001418146,0.8053682,0.003306264,0.04650386,0.0001449837,0.1400129],"study_design_scores_gemma":[0.00525832,0.0006206662,0.006726777,0.0009337271,0.0004204366,0.0002057871,0.001672093,0.247616,0.7117313,0.01092414,0.01312751,0.0007632119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5741908,0.0002433819,0.4233995,0.001800766,0.0001693901,0.00008964268,0.000004681877,0.0000478443,0.00005397987],"genre_scores_gemma":[0.9721578,0.0008723764,0.02678821,0.00008032289,0.00006807804,0.000006214153,0.00000298647,0.0000111158,0.00001292528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.708425,"threshold_uncertainty_score":0.3320679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0112876974406414,"score_gpt":0.2174283639608117,"score_spread":0.2061406665201703,"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."}}