{"id":"W3032399209","doi":"10.1115/1.4047353","title":"Recent Advancements in Machining With Abrasives","year":2020,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"Advanced Surface Polishing Techniques","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Abrasive; Machining; Grinding; Abrasive machining; Boron nitride; Manufacturing engineering; Mechanical engineering; Sustainability; Process (computing); Focus (optics); Materials science; Computer science; Engineering; Nanotechnology","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":[0.0002955918,0.000117026,0.0001683346,0.0002015041,0.00003301614,0.00005890788,0.0002153232,0.00002048859,0.000003053661],"category_scores_gemma":[0.0001061896,0.00009868066,0.00001225117,0.0002444483,0.00004307353,0.0009054848,0.000037325,0.0002964821,4.206337e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001018399,"about_ca_system_score_gemma":0.00002466309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.511416e-7,"about_ca_topic_score_gemma":7.208246e-7,"domain_scores_codex":[0.9991157,0.000003337426,0.0002225768,0.0001059894,0.000306535,0.0002458793],"domain_scores_gemma":[0.999671,0.00002797725,0.000051443,0.00006308108,0.00004104643,0.0001453881],"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":[0.00001828713,0.000005535463,0.001197562,0.00008224528,0.00001146235,0.00006282127,0.001214834,0.9143072,0.03441074,0.00002447194,0.00002599283,0.04863881],"study_design_scores_gemma":[0.001159829,0.0003866266,0.04293053,0.0007743954,0.00001793217,0.0002012956,0.000568565,0.08770248,0.8493587,0.0001496933,0.01604826,0.0007016719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9815078,0.0005613202,0.01728619,0.0002300409,0.0001021684,0.00005002049,4.886912e-7,0.00009802267,0.0001639255],"genre_scores_gemma":[0.9703749,0.0007206883,0.02878846,0.00005625626,0.00004107839,0.000001088759,7.071918e-8,0.00001678047,6.917998e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8266048,"threshold_uncertainty_score":0.402408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059147944669667,"score_gpt":0.2218279849770099,"score_spread":0.2112365055303132,"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."}}