{"id":"W2789646893","doi":"10.5604/01.3001.0010.8811","title":"INTELLIGENT MACHINING: REAL-TIME TOOL CONDITION MONITORING AND INTELLIGENT ADAPTIVE CONTROL SYSTEMS","year":2018,"lang":"en","type":"article","venue":"Journal of Machine Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"","keywords":"Machining; Machine tool; Process (computing); Reliability (semiconductor); Engineering; Controller (irrigation); Computer science; Condition monitoring; Manufacturing engineering; Control engineering; Reliability 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":[0.0004117488,0.0006451489,0.0005292552,0.0005327607,0.0001600483,0.001156098,0.0009472074,0.0009825279,0.001784347],"category_scores_gemma":[0.0008488509,0.0002112111,0.0002381131,0.0007891813,0.0005149203,0.001461708,0.0005675693,0.0008554148,0.0007559893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003189499,"about_ca_system_score_gemma":0.000285565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008884845,"about_ca_topic_score_gemma":0.0005172594,"domain_scores_codex":[0.999422,0.00008236761,0.00002964112,0.0001497464,0.0002798671,0.00003644884],"domain_scores_gemma":[0.9996407,0.0001078153,0.00007583412,0.00005124156,0.000103723,0.00002079379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002065505,0.0001130271,0.001272846,0.0006942273,0.00007342861,0.0002208307,0.0001520536,0.04911764,0.03861149,0.02669184,0.01032041,0.8725257],"study_design_scores_gemma":[0.00006855997,0.0006075609,0.00589071,0.0002444955,0.000104994,0.0009093914,0.0001181016,0.7436993,0.02658055,0.05849509,0.1631368,0.0001443174],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01061237,0.02664969,0.9417419,0.00131743,0.0006727921,0.00009233259,0.0001178599,0.003218076,0.0155776],"genre_scores_gemma":[0.7265573,0.02689384,0.2204842,0.001169941,0.002096248,0.0002254639,0.0006925446,0.0002224264,0.02165803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001784347,"threshold_uncertainty_score":0.005969286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007663274685495045,"score_gpt":0.2336723380272998,"score_spread":0.2260090633418047,"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."}}