{"id":"W2133020076","doi":"10.1243/1748006xjrr93","title":"Maintenance modelling and scheduling in fault-tolerant control","year":2008,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Fault tolerance; Scheduling (production processes); Markov chain; Computer science; Optimal maintenance; Fault detection and isolation; Mathematical optimization; Control theory (sociology); Control (management); Distributed computing; Engineering; Reliability engineering; Mathematics; Actuator; Artificial intelligence; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.001073189,0.0001235384,0.0003781379,0.00008164321,0.00005908581,0.000006398787,0.0001299002,0.0001183125,0.000001005682],"category_scores_gemma":[0.000750764,0.00008435783,0.0001001679,0.0001868153,0.0002185884,0.00026936,0.00002486025,0.00040357,6.418176e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005392169,"about_ca_system_score_gemma":0.00003025546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001323095,"about_ca_topic_score_gemma":6.520899e-7,"domain_scores_codex":[0.9988128,0.00001235461,0.0006975031,0.0001176615,0.0002110501,0.000148657],"domain_scores_gemma":[0.9991803,0.00008887717,0.0002516526,0.00006922754,0.0003378242,0.00007208942],"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":[0.0001034544,0.00004040181,0.001778753,0.0001801898,0.00001411516,4.950214e-7,0.0002785677,0.9941765,0.0009833309,0.002180103,0.00001056303,0.0002535408],"study_design_scores_gemma":[0.001204797,0.0000751292,0.0009859934,0.0004593252,0.00004080156,0.00005498409,0.0001702967,0.9887233,0.005183594,0.002869023,0.000130084,0.0001026554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8562114,0.0004720586,0.1427824,0.0001040101,0.0002337204,0.0001392806,0.000005309612,0.00001197407,0.00003990202],"genre_scores_gemma":[0.9835525,0.005268966,0.01112948,0.000005892062,0.00003213211,0.000002081595,1.23005e-7,0.000007202132,0.000001697198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1316529,"threshold_uncertainty_score":0.3440013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007165396534082781,"score_gpt":0.1837241743250642,"score_spread":0.1765587777909814,"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."}}