{"id":"W4230454473","doi":"10.1002/9781118445112.stat03643","title":"Condition Monitoring","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Condition monitoring; Fault (geology); Predictive maintenance; Data acquisition; Condition-based maintenance; Preventive maintenance; Fault detection and isolation; Computer science; Reliability engineering; Engineering; Noise (video); Artificial intelligence; Electrical 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":[0.001669152,0.001467212,0.0009374053,0.004531203,0.001030811,0.003071228,0.00194722,0.001230352,0.1072132],"category_scores_gemma":[0.006496992,0.0003569536,0.000566933,0.00325094,0.0004346895,0.002711172,0.001916159,0.001113679,0.05390531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001215989,"about_ca_system_score_gemma":0.00127029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003786508,"about_ca_topic_score_gemma":0.002195464,"domain_scores_codex":[0.9966889,0.0003261179,0.0001994911,0.0009504794,0.001641527,0.0001935183],"domain_scores_gemma":[0.9949032,0.0006661126,0.0003930361,0.0009992796,0.002852694,0.0001855889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005700737,0.0001525179,0.009796475,0.0006495283,0.00005469626,0.0002436379,0.0003098354,0.002742543,0.01191423,0.01061091,0.2259062,0.7370492],"study_design_scores_gemma":[0.0000889132,0.0003198744,0.01782487,0.0004670071,0.0001081397,0.0006702787,0.000340655,0.0194737,0.03311421,0.007969102,0.9194584,0.0001648285],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.03277384,0.004575768,0.2607818,0.00270456,0.004306053,0.002285327,0.05039024,0.05471667,0.5874659],"genre_scores_gemma":[0.3987462,0.004108544,0.1352071,0.003277656,0.001700079,0.001656559,0.05487739,0.004259886,0.3961666],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1072132,"threshold_uncertainty_score":0.358664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980934888994104,"score_gpt":0.3203387114831999,"score_spread":0.3005293625932589,"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."}}