{"id":"W4390846800","doi":"10.1504/ijmmm.2023.136037","title":"Numerical study on the impacts of tool edge geometry and cutting conditions in orthogonal machining of AISI 1045 steel","year":2023,"lang":"en","type":"article","venue":"International Journal of Machining and Machinability of Materials","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Machining; Enhanced Data Rates for GSM Evolution; Geometry; Mechanical engineering; Engineering drawing; Structural engineering; Materials science; Engineering; 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":[0.00197821,0.0001542771,0.0004461051,0.0003727362,0.00004915915,0.0000329747,0.0002253396,0.00005221881,0.00002787788],"category_scores_gemma":[0.001086227,0.000119325,0.00005862202,0.0001914378,0.00009359181,0.0001954083,0.00008770987,0.0002298965,2.246704e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003127411,"about_ca_system_score_gemma":0.00003801115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005026536,"about_ca_topic_score_gemma":0.000003166347,"domain_scores_codex":[0.9981745,0.0001652041,0.0009921746,0.0001367854,0.0003871082,0.0001441605],"domain_scores_gemma":[0.9983527,0.0007679494,0.0005300638,0.0001155113,0.000190486,0.00004330577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001146911,0.0006567264,0.477353,0.0009735888,0.0006914171,0.00004579321,0.01376164,0.3853574,0.1013039,0.004605287,0.00005115383,0.01405327],"study_design_scores_gemma":[0.002552549,0.0009660735,0.933628,0.0009819171,0.00008651158,0.00009721655,0.003332166,0.0229748,0.032,0.003049558,0.00002747183,0.0003036878],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981566,0.0001128321,0.0008477147,0.000156121,0.0003774639,0.0001510739,0.0000960489,0.00002093399,0.00008122333],"genre_scores_gemma":[0.9993563,0.00007840528,0.0004536922,0.00001784216,0.00005665387,0.000004347554,0.00001141006,0.00001886529,0.000002505951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.456275,"threshold_uncertainty_score":0.4865932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01353434182374549,"score_gpt":0.3003545024612684,"score_spread":0.2868201606375229,"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."}}