{"id":"W4232955327","doi":"10.32920/ryerson.14648742","title":"Inspection and maintenance optimisation of multicomponent systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Downtime; Reliability engineering; Component (thermodynamics); Reliability (semiconductor); Computer science; Preventive maintenance; Type (biology); Monte Carlo method; Corrective maintenance; Engineering; Mathematics; Statistics","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":[0.0001293386,0.0001431564,0.0002766924,0.000065875,0.00001875031,0.0000530622,0.00005552898,0.000187735,0.00000921249],"category_scores_gemma":[0.00003634634,0.0001387771,0.00004604199,0.00005600116,0.00003836463,0.00008142059,0.00008382239,0.0001911757,8.02323e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001151141,"about_ca_system_score_gemma":0.00001747633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002320022,"about_ca_topic_score_gemma":0.00001680956,"domain_scores_codex":[0.9992341,0.00002419766,0.0003196279,0.0002089228,0.0001061149,0.00010704],"domain_scores_gemma":[0.9994829,0.00002218043,0.00007048746,0.0002462064,0.0001444571,0.00003381834],"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":[0.000003766933,0.00001635319,0.00008854285,0.001097015,0.00003072531,7.414675e-7,0.0002431574,0.9953252,0.001919361,0.0006193719,0.0001307956,0.0005249326],"study_design_scores_gemma":[0.000152162,0.000008487568,0.001185028,0.0004177384,0.00001901538,0.000004861736,0.0004521885,0.9955425,0.001915802,0.00006334912,0.00009193343,0.0001468833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4780313,0.001407726,0.5130476,0.00006046498,0.00191306,0.0005872577,0.00001231281,0.0004644073,0.004475891],"genre_scores_gemma":[0.9840732,0.002519376,0.0131516,0.000003942318,0.00003774197,0.00004036648,0.00006392508,0.0000200186,0.00008979174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5060419,"threshold_uncertainty_score":0.5659165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00911424362121756,"score_gpt":0.1938939113361927,"score_spread":0.1847796677149751,"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."}}