{"id":"W3033721775","doi":"10.1109/tr.2020.2995277","title":"State-Based Opportunistic Maintenance With Multifunctional Maintenance Windows","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fok Ying Tung Education Foundation; National Natural Science Foundation of China","keywords":"Reliability engineering; Initialization; Spare part; Scheduling (production processes); Maintenance engineering; Computer science; Probabilistic logic; Maintenance actions; Preventive maintenance; Interval (graph theory); Software maintenance; Corrective maintenance; Engineering; Real-time computing; Operations management; Mathematics; Artificial intelligence","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":[0.0007187562,0.0005476494,0.0005757564,0.0003955073,0.0002741113,0.000604216,0.00112276,0.0004798168,0.001144112],"category_scores_gemma":[0.002233692,0.0002388681,0.0003139063,0.000292649,0.0004125029,0.0008469764,0.0006577486,0.00045689,0.0001503647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005018366,"about_ca_system_score_gemma":0.0006557754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001763799,"about_ca_topic_score_gemma":0.001914499,"domain_scores_codex":[0.9994447,0.00009232215,0.0000419038,0.0001803134,0.0001262641,0.0001145177],"domain_scores_gemma":[0.9984437,0.0006038781,0.0003756264,0.0002688239,0.0001720102,0.0001360272],"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":[0.0005422864,0.0002370783,0.004028699,0.00009620428,0.00005685571,0.0001838911,0.0001602528,0.8475422,0.01965053,0.01335183,0.001087871,0.1130624],"study_design_scores_gemma":[0.00001721236,0.0001469528,0.0009956034,0.000006113318,0.00002122166,0.00006522396,0.00001452099,0.9926518,0.002378924,0.003197657,0.0004949446,0.000009824814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2341349,0.0003952121,0.7614315,0.0001746474,0.00005876573,0.00009905803,0.0001221069,0.0008896526,0.002694207],"genre_scores_gemma":[0.9877248,0.00004174873,0.01156022,0.00001738346,0.000009639624,0.00002309312,0.00002901815,0.00001252345,0.000581486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001763799,"threshold_uncertainty_score":0.003827453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348958919451938,"score_gpt":0.1960511003331629,"score_spread":0.1825615111386435,"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."}}