{"id":"W2746245043","doi":"10.1007/s10845-017-1353-z","title":"Multi-phase sequential preventive maintenance scheduling for deteriorating repairable systems","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Preventive maintenance; Reliability engineering; Operability; Scheduling (production processes); Optimal maintenance; Schedule; Engineering; Predictive maintenance; Computer science; Operations research; Mathematical optimization; Operations management; Mathematics","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":[0.0008315817,0.0001992913,0.0003606415,0.0001117142,0.0003225939,0.0003643286,0.0003931723,0.00009820017,0.00001187217],"category_scores_gemma":[0.0004407545,0.0001736381,0.0002518816,0.00002011969,0.00004961942,0.0007687563,0.00005051966,0.000273701,0.000004518427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002370644,"about_ca_system_score_gemma":0.0000348566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001396058,"about_ca_topic_score_gemma":0.0000071193,"domain_scores_codex":[0.9984811,0.00002797177,0.0008066604,0.000165987,0.0001787722,0.0003395375],"domain_scores_gemma":[0.9985597,0.00006643537,0.0006715897,0.0003409313,0.0002545598,0.0001067621],"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":[0.0001376504,0.00007421431,0.0000446774,0.0004799496,0.0001661717,0.00002932413,0.0003985957,0.9716961,0.01482172,0.00006512563,0.0001454227,0.01194106],"study_design_scores_gemma":[0.001426927,0.000160828,0.0001019767,0.001133318,0.00006985321,0.00008954956,0.0005714356,0.7302253,0.263041,0.0001828741,0.002715283,0.0002815951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3426851,0.0002721115,0.6546361,0.00003062089,0.001867766,0.0003319961,0.000006947891,0.0000461105,0.0001232246],"genre_scores_gemma":[0.9282773,0.0003189782,0.07073594,0.000008081267,0.0003503794,0.00001718442,0.000002434972,0.00004058285,0.0002491908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5855921,"threshold_uncertainty_score":0.7080755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03458222179374398,"score_gpt":0.3021643902928102,"score_spread":0.2675821684990662,"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."}}