{"id":"W2765605542","doi":"10.1115/1.4038206","title":"Design for Manufacturing of Variable Microgeometry Cutting Tools","year":2017,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Ontario Tech University","funders":"","keywords":"Machining; Enhanced Data Rates for GSM Evolution; Mechanical engineering; Materials science; Wedge (geometry); Surface roughness; Surface integrity; Process (computing); Cutting tool; Engineering drawing; Computer science; Engineering; Composite material; Geometry; Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000426505,0.0005623609,0.0003937212,0.0004124946,0.0002485752,0.0007504234,0.001215365,0.0006713847,0.001009863],"category_scores_gemma":[0.0007170444,0.0003467883,0.0003808663,0.0002399704,0.0003227551,0.0003495054,0.0004634223,0.000356149,0.0003521746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003708413,"about_ca_system_score_gemma":0.0005459966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003954225,"about_ca_topic_score_gemma":0.0004634581,"domain_scores_codex":[0.9994714,0.0000530886,0.00003841087,0.0001327794,0.0002461271,0.00005830464],"domain_scores_gemma":[0.9994895,0.00007283856,0.0002027765,0.00008100181,0.0001265347,0.0000272929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002623462,0.00009342867,0.002476152,0.0009144514,0.00005232004,0.0004311698,0.0002708996,0.1514834,0.6513212,0.01326536,0.001182132,0.1782473],"study_design_scores_gemma":[0.0001185878,0.001910612,0.007813519,0.0001047733,0.00009460968,0.001226532,0.000114665,0.6844481,0.2685933,0.003778477,0.03169155,0.0001053287],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05131808,0.0003596666,0.9437603,0.00005928608,0.00004855963,0.000230399,0.00006934816,0.000591416,0.003562872],"genre_scores_gemma":[0.5403847,0.0001949153,0.4576212,0.00003562172,0.00001557351,0.000216451,0.00007786987,0.00004795401,0.001405693],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001215365,"threshold_uncertainty_score":0.003378272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01784193893466332,"score_gpt":0.2417072887833386,"score_spread":0.2238653498486753,"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."}}