{"id":"W4367022783","doi":"10.1007/978-3-031-28839-5_41","title":"Environmental Assessment and Optimization When Machining with Micro-textured Cutting Tools","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Advanced Machining and Optimization Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machining; Mechanical engineering; Surface roughness; Tool wear; Cutting tool; Groove (engineering); Surface finish; Spiral (railway); Materials science; Engineering drawing; Manufacturing engineering; Engineering; Composite material","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001414223,0.0001978447,0.0002564985,0.0003283247,0.0001023766,0.0004045655,0.0002263754,0.0002593698,0.0006590108],"category_scores_gemma":[0.0002004688,0.00008537451,0.0002497325,0.0004379191,0.000119173,0.0002992769,0.000157149,0.000148106,0.0001238789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002131111,"about_ca_system_score_gemma":0.0001039371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004224249,"about_ca_topic_score_gemma":0.001268978,"domain_scores_codex":[0.9998096,0.00001958862,0.000007133083,0.00002829618,0.0001185544,0.00001683886],"domain_scores_gemma":[0.9998977,0.00003546015,0.0000199144,0.00001079305,0.00003250343,0.00000356842],"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":[0.0002503332,0.0001098871,0.006070023,0.0005442516,0.00002892052,0.0002223889,0.00007286292,0.03572505,0.8074273,0.0002728354,0.000248293,0.1490279],"study_design_scores_gemma":[0.00001909053,0.001648596,0.04032255,0.00003674461,0.00006103337,0.0003424641,0.0002179588,0.07793223,0.8724979,0.0008580895,0.006021942,0.00004142725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9629697,0.002163567,0.02966574,0.00003148136,0.00001692229,0.00005134182,0.0001983632,0.0001435102,0.004759341],"genre_scores_gemma":[0.9848667,0.0004491025,0.01295484,0.00001135683,0.000002354849,0.00001373378,0.0001144873,0.00001877483,0.001568644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006590108,"threshold_uncertainty_score":0.002204657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008043982199299856,"score_gpt":0.2127369794305476,"score_spread":0.2046929972312477,"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."}}