{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001135398,0.0009362396,0.001293539,0.0008216543,0.0003595671,0.001175875,0.001471628,0.001310329,0.001812504],"category_scores_gemma":[0.003872583,0.0007149459,0.0009529319,0.0009328133,0.001185651,0.0009674541,0.0007158485,0.0009042089,0.0001628151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001636287,"about_ca_system_score_gemma":0.001365646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008687332,"about_ca_topic_score_gemma":0.004429955,"domain_scores_codex":[0.999402,0.0001638326,0.00002552348,0.0001565939,0.0001588835,0.0000930625],"domain_scores_gemma":[0.9979876,0.001459984,0.0003205698,0.00005856803,0.0001025003,0.00007058935],"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":[0.000009881573,0.00001242378,0.0001595423,0.00001982761,0.000008965549,0.0000168867,0.00001073996,0.9956442,0.0002232055,0.002086428,0.00004064316,0.001767237],"study_design_scores_gemma":[0.00000309391,0.000008637761,0.00006416448,0.00000216162,0.000002730872,0.000003966126,0.000002411312,0.9985654,0.00008244682,0.00120894,0.0000544681,0.000001628744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09009625,0.0005567009,0.9046595,0.0002350454,0.00003430407,0.0001001856,0.0001229188,0.0001831716,0.004011935],"genre_scores_gemma":[0.9203914,0.0004767399,0.07425093,0.000050108,0.00001976933,0.0002186885,0.0001119017,0.00005108971,0.004429307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008687332,"threshold_uncertainty_score":0.01727349,"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."}}