{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001054982,0.000665855,0.0006880153,0.000437788,0.00004626823,0.00007369171,0.0004431086,0.0004924507,0.002551633],"category_scores_gemma":[0.0001174346,0.0006991202,0.00005084851,0.0001688819,0.00009539537,0.00005390221,0.00007346416,0.0007706492,0.0004595743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001607314,"about_ca_system_score_gemma":0.00004516197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002049991,"about_ca_topic_score_gemma":0.0003617684,"domain_scores_codex":[0.9979456,0.00006106465,0.0005295679,0.0004729105,0.0004648457,0.0005259818],"domain_scores_gemma":[0.998633,0.0001685504,0.000213488,0.0006889417,0.00009134733,0.0002046989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002842803,0.0000718457,0.0002364297,0.0005663279,0.00009825176,0.00002622699,0.00001836019,0.0001078711,0.000121436,0.003584279,0.9634045,0.03176168],"study_design_scores_gemma":[0.0003307887,0.000111593,0.0003993897,0.001520651,0.0001005477,0.000005498902,0.00001165831,0.005010956,0.0003028706,0.002923884,0.9882802,0.001002001],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001535763,0.003314701,0.590029,0.00003391308,0.002566839,0.001199324,0.07294788,0.009531412,0.3202233],"genre_scores_gemma":[0.009643822,0.03127417,0.7593078,0.000130032,0.003368554,0.0004312396,0.03606916,0.004405908,0.1553693],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1692788,"threshold_uncertainty_score":0.999546,"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."}}