{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009413256,0.0001315235,0.0002316457,0.000257223,0.0002728226,0.0002275383,0.0004025169,0.00004400976,0.000002194824],"category_scores_gemma":[0.0003855588,0.0001208604,0.00003752768,0.00005631661,0.0000651733,0.001343798,0.00005665141,0.0001526292,1.50077e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005692408,"about_ca_system_score_gemma":0.00003523921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002073925,"about_ca_topic_score_gemma":6.181813e-8,"domain_scores_codex":[0.9990588,0.000002265142,0.0003242618,0.0001164165,0.000226225,0.0002719925],"domain_scores_gemma":[0.9992643,0.0001242688,0.0002326766,0.0001724135,0.0001173192,0.00008902607],"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":[0.000009190681,0.000003937157,0.00002923549,0.0002861786,0.00001817809,0.000001839717,0.00009756342,0.9455453,0.03575771,0.00003658923,0.000008669976,0.01820565],"study_design_scores_gemma":[0.0003284716,0.00005586444,0.001723045,0.0002528794,0.00002269638,0.00004864357,0.00003930048,0.1192502,0.8774295,0.0001780483,0.0005164811,0.0001547942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3969559,0.000220628,0.6022705,0.00001228726,0.0003652035,0.00006896122,0.000001537928,0.00002509293,0.00007988643],"genre_scores_gemma":[0.809944,0.0001318887,0.1898078,0.000004902256,0.00008481533,0.000001843178,1.821234e-7,0.00001813862,0.000006399419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8416719,"threshold_uncertainty_score":0.4928542,"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."}}