{"id":"W1985100752","doi":"10.1016/j.ijmachtools.2006.09.019","title":"An expert troubleshooting system for the milling process","year":2007,"lang":"en","type":"article","venue":"International Journal of Machine Tools and Manufacture","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Troubleshooting; Expert system; Inference engine; Cutting tool; Robustness (evolution); Vibration; Engineering; Process (computing); Tool wear; Computer science; Machining; Mechanical engineering; Artificial intelligence; Reliability engineering; Acoustics","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":[0.0009241319,0.001320343,0.001062392,0.001426657,0.0005277668,0.0007805392,0.001736506,0.001000464,0.04046764],"category_scores_gemma":[0.00270716,0.0006669873,0.0003440118,0.0007007389,0.0002503001,0.00101192,0.000736755,0.0006841143,0.008068015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003358816,"about_ca_system_score_gemma":0.000974767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001822661,"about_ca_topic_score_gemma":0.002317382,"domain_scores_codex":[0.9991007,0.000122938,0.00007374946,0.0002795174,0.0003693999,0.00005366779],"domain_scores_gemma":[0.9968611,0.001495087,0.0001867897,0.0005548353,0.0007244969,0.0001775946],"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":[0.001608336,0.0004278707,0.003092771,0.0006435786,0.00008204516,0.0004760387,0.000316083,0.009200535,0.08773991,0.0009287467,0.04458859,0.8508955],"study_design_scores_gemma":[0.001101568,0.001437344,0.02598844,0.000277794,0.0003593377,0.00350966,0.0002493158,0.5224823,0.264244,0.004026146,0.1759374,0.0003867895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03909892,0.0005469784,0.7507952,0.0001364497,0.0001099372,0.0005923425,0.002579507,0.1981445,0.007996171],"genre_scores_gemma":[0.3054444,0.0004693975,0.6446217,0.0004625548,0.0001170059,0.0006678322,0.007542243,0.00636925,0.03430573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04046764,"threshold_uncertainty_score":0.1353778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01035453332720781,"score_gpt":0.2895572288539422,"score_spread":0.2792026955267344,"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."}}