{"id":"W2340761181","doi":"10.1109/rams.2016.7448007","title":"Modeling failure and maintenance effects of a system subject to multiple preventive maintenance types","year":2016,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Preventive maintenance; Unavailability; Corrective maintenance; Reliability engineering; Planned maintenance; Reliability (semiconductor); Downtime; Process (computing); Engineering; Proactive maintenance; Risk analysis (engineering); Computer science; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00405975,0.001636886,0.001360052,0.001801706,0.0006193798,0.0013677,0.002796153,0.00296289,0.003303364],"category_scores_gemma":[0.009408006,0.001100429,0.001940347,0.0008504872,0.001726227,0.001781335,0.0015091,0.001821299,0.0004501716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002531695,"about_ca_system_score_gemma":0.001747491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05260661,"about_ca_topic_score_gemma":0.02071603,"domain_scores_codex":[0.9985543,0.0004383721,0.00007922435,0.0003557076,0.0002520959,0.000320272],"domain_scores_gemma":[0.990983,0.006326677,0.001419992,0.0002606737,0.0007197757,0.0002897846],"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":[0.00005348603,0.00005087292,0.002987286,0.00004538816,0.0000387689,0.0001289998,0.00007026806,0.9881076,0.0004640767,0.005276375,0.0001873013,0.002589619],"study_design_scores_gemma":[0.000009707753,0.00003742203,0.0009683287,0.000006255387,0.00002812248,0.00002363536,0.00001634295,0.9969563,0.00009237165,0.001743139,0.0001095693,0.000008687686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.489651,0.001773553,0.4968604,0.001778261,0.0001584461,0.0003451514,0.0009164228,0.0006768255,0.007840018],"genre_scores_gemma":[0.9828674,0.0005770877,0.008353494,0.00007176916,0.0000783764,0.0001726622,0.0002375176,0.00004136535,0.00760032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05260661,"threshold_uncertainty_score":0.1046008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002884141048698975,"score_gpt":0.1703630600407396,"score_spread":0.1674789189920406,"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."}}