{"id":"W2736149070","doi":"10.1080/10910344.2017.1336177","title":"Cutting tool wear detection using multiclass logical analysis of data","year":2017,"lang":"en","type":"article","venue":"Machining Science and Technology","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Machining; Tool wear; Artificial neural network; Machine tool; Computer science; Pattern recognition (psychology); Artificial intelligence; Class (philosophy); Numerical control; Machine learning; Data mining; Engineering; Mechanical engineering","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":[0.0007717608,0.0005166082,0.0006036205,0.003140955,0.0003527284,0.0009987555,0.0004916694,0.0003670297,0.0008290909],"category_scores_gemma":[0.003261082,0.0001653659,0.000508919,0.001410891,0.0003838278,0.001044932,0.0005043569,0.0003772732,0.0002966187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003764792,"about_ca_system_score_gemma":0.0005012199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001059089,"about_ca_topic_score_gemma":0.001452674,"domain_scores_codex":[0.9988062,0.0001796082,0.0001126439,0.000268004,0.0005654141,0.00006801035],"domain_scores_gemma":[0.997777,0.0008925098,0.0004690003,0.0002532776,0.0005483352,0.0000597875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004445189,0.0002764608,0.03300569,0.0004550764,0.0001713601,0.0001863373,0.0003312566,0.01620838,0.08347306,0.00236583,0.0009403719,0.8621417],"study_design_scores_gemma":[0.00005107682,0.0007048605,0.05726996,0.00009254121,0.000139959,0.001003242,0.0004600548,0.8424681,0.08214805,0.006088032,0.009442382,0.0001316926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1088467,0.0004261309,0.8879051,0.00008105974,0.00005867988,0.0001460263,0.0003079324,0.000768037,0.001460459],"genre_scores_gemma":[0.6811292,0.0002842545,0.3167446,0.00005269585,0.00004909415,0.000195064,0.0005135066,0.00005127482,0.0009801746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003140955,"threshold_uncertainty_score":0.004081488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03828601625148307,"score_gpt":0.3259635467418037,"score_spread":0.2876775304903206,"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."}}