{"id":"W4362647392","doi":"10.1109/rams51473.2023.10088213","title":"Dynamic Multilevel Redundancy Allocation Optimization Under Uncertainty","year":2023,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Redundancy (engineering); Computer science; Reliability engineering; Process (computing); Variety (cybernetics); Key (lock); Risk analysis (engineering); Systems engineering; Industrial engineering; Operations research; Engineering; 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":[0.001006413,0.00109189,0.001379453,0.000777033,0.0004529758,0.001199623,0.0008620445,0.001019192,0.003245905],"category_scores_gemma":[0.002289843,0.0005242504,0.0007949648,0.0006910665,0.0007199692,0.0007916617,0.001202951,0.0009333472,0.0003562358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001025937,"about_ca_system_score_gemma":0.0009319222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004361821,"about_ca_topic_score_gemma":0.002185995,"domain_scores_codex":[0.9995111,0.0001590394,0.00001818638,0.00009482096,0.0001099397,0.0001067982],"domain_scores_gemma":[0.999056,0.0005636741,0.0001427658,0.00004016264,0.0001483669,0.00004897763],"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":[0.00005264634,0.00001252978,0.0001957439,0.00007167875,0.00002709156,0.00007541946,0.00002231956,0.9867737,0.0007967872,0.004907837,0.0004234119,0.006640746],"study_design_scores_gemma":[0.00001076195,0.0000287004,0.0001271718,0.000008170025,0.00001096048,0.0000143004,0.000008692327,0.9963862,0.0001804724,0.002906562,0.0003132448,0.000004881811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08470695,0.001752078,0.8933731,0.0006565671,0.00008796289,0.0001260235,0.0002782165,0.0003449077,0.01867426],"genre_scores_gemma":[0.9555773,0.0005428872,0.03873021,0.0001040082,0.00002923197,0.0001729363,0.0001692612,0.00005556101,0.004618552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004361821,"threshold_uncertainty_score":0.0108586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009790343237056494,"score_gpt":0.2282290386144137,"score_spread":0.2184386953773572,"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."}}