{"id":"W4233512660","doi":"10.1002/9780470061596.risk0502","title":"Condition Monitoring","year":2008,"lang":"en","type":"other","venue":"Encyclopedia of Quantitative Risk Analysis and Assessment","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Condition monitoring; Fault (geology); Predictive maintenance; Condition-based maintenance; Data acquisition; Preventive maintenance; Computer science; Reliability engineering; Fault detection and isolation; Noise (video); Engineering; 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"],"consensus_categories":[],"category_scores_codex":[0.000148421,0.0003130779,0.000764415,0.0009182388,0.00004145536,0.00001491714,0.000118171,0.0001599848,0.0003610483],"category_scores_gemma":[0.00002558535,0.0002956513,0.0002184287,0.0005354758,0.00007748526,0.00005815822,0.00003282656,0.0003008975,0.000005993759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004418911,"about_ca_system_score_gemma":0.00001996638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007472169,"about_ca_topic_score_gemma":0.0001737076,"domain_scores_codex":[0.9988027,0.00008911195,0.000380217,0.0002806488,0.0002793359,0.000168051],"domain_scores_gemma":[0.9991743,0.0001568534,0.0003028298,0.0002572393,0.00004022631,0.00006852877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004489464,0.0002520867,0.6545517,0.0005619761,0.01216158,0.00002994343,0.000602827,0.001517456,0.0001065244,0.002608214,0.2964195,0.03118369],"study_design_scores_gemma":[0.0005208167,0.0002637295,0.393698,0.0005515001,0.006016121,0.000001526673,0.0003597138,0.01401909,0.0007553864,0.0004224008,0.5820212,0.001370532],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"review","genre_scores_codex":[0.01136795,0.00992489,0.05420669,0.00001069603,0.0002664068,0.0003972564,0.0005958068,0.0006299115,0.9226004],"genre_scores_gemma":[0.08890066,0.7207417,0.1639796,0.000004402008,0.0002871846,0.0002099541,0.0003795967,0.0005217566,0.02497511],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8976253,"threshold_uncertainty_score":0.9999496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181233461315601,"score_gpt":0.3381826292104562,"score_spread":0.3263702945973002,"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."}}