{"id":"W1971652201","doi":"10.1007/s10845-009-0352-0","title":"Minor maintenance actions and their impact on diagnostic and prognostic CBM models","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Minor (academic); Reliability (semiconductor); Reliability engineering; Maintenance actions; Condition-based maintenance; Condition monitoring; Optimal maintenance; Engineering; Preventive maintenance; Predictive maintenance; Oil analysis; Computer science; Petroleum 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":[0.002755014,0.0006204791,0.0006767823,0.0006559664,0.0005227261,0.0009448414,0.0007932145,0.00119468,0.001317234],"category_scores_gemma":[0.03266345,0.0003267222,0.0004090688,0.0003712539,0.0007510619,0.0008407792,0.0005388201,0.001149305,0.00009331752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006993009,"about_ca_system_score_gemma":0.0007143507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005077796,"about_ca_topic_score_gemma":0.003728913,"domain_scores_codex":[0.9992104,0.0002669413,0.00005568421,0.000135989,0.0002008649,0.0001302654],"domain_scores_gemma":[0.9541717,0.04161176,0.00131009,0.001336363,0.00115836,0.0004116317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009938861,0.0001280866,0.009987072,0.00005054494,0.00003796393,0.0002145628,0.00005034414,0.9697724,0.002921673,0.002507641,0.00025658,0.01307925],"study_design_scores_gemma":[0.000009892584,0.00006115642,0.00289602,0.000004826256,0.00002205614,0.00005411611,0.00001248731,0.9938282,0.001767702,0.001281958,0.00005281826,0.000008660931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9404427,0.0004112852,0.0562299,0.0004510716,0.00007416306,0.00002991251,0.0003269673,0.0004203597,0.001613556],"genre_scores_gemma":[0.998423,0.00002843134,0.001316861,0.00001020332,0.000004074102,0.000004194726,0.00005227359,0.000008734143,0.0001523007],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005077796,"threshold_uncertainty_score":0.01457012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660786198893364,"score_gpt":0.2734194018194332,"score_spread":0.2568115398304996,"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."}}