{"id":"W2999862314","doi":"10.3390/app10020498","title":"Modeling the Optimal Maintenance Scheduling Strategy for Bridge Networks","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Elevator Systems and Control","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Basic Research Program of Shaanxi Province; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Optimal maintenance; Mathematical optimization; Scheduling (production processes); Job shop scheduling; Simulated annealing; Nonlinear programming; Operations research; Nonlinear system; Engineering; Mathematics; Computer network; Routing (electronic design automation)","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":[0.0002903957,0.00009119028,0.0001110957,0.000009753749,0.0002419095,0.0001191121,0.0003086581,0.00003225408,0.000002966661],"category_scores_gemma":[0.000008463877,0.00006089919,0.00003746888,0.0001807182,0.00006155215,0.00006799494,0.00001716248,0.00009730646,0.000006142927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008052808,"about_ca_system_score_gemma":0.00001935247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007959863,"about_ca_topic_score_gemma":0.000004511591,"domain_scores_codex":[0.9992641,0.00000436952,0.0001507033,0.0001769898,0.0001166529,0.0002872219],"domain_scores_gemma":[0.9997876,0.00004569697,0.00001721936,0.00007848671,0.00001630812,0.0000546987],"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":[0.000003206625,8.549941e-7,0.000006695742,0.000008846879,0.000006436369,1.892061e-7,0.0001452375,0.981335,0.001224142,0.01490762,0.0001082935,0.002253431],"study_design_scores_gemma":[0.0001330284,0.0000182513,0.000008706107,0.000006972543,0.000004492729,6.929211e-7,0.0005418028,0.9986627,0.0001295523,0.0001367227,0.0002658193,0.00009127069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1614605,0.0005656913,0.8348998,0.000338911,0.0001328782,0.000277832,0.000002007039,0.0001346168,0.002187645],"genre_scores_gemma":[0.9976022,0.000009483948,0.001683512,0.000262542,0.0003480493,0.000077948,5.679452e-7,0.0000106827,0.00000497945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8361417,"threshold_uncertainty_score":0.2483397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03838932872342396,"score_gpt":0.2360749528076528,"score_spread":0.1976856240842289,"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."}}