{"id":"W4403487983","doi":"10.3390/engproc2024076021","title":"Enhancing Seismic Resilience of Bridge Infrastructure Using Bayesian Belief Network Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Regina","funders":"","keywords":"Bridge (graph theory); Bayesian network; Resilience (materials science); Computer science; Bayesian probability; Critical infrastructure; Computer security; 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":[0.002986251,0.0006172968,0.00049917,0.002467679,0.000330296,0.001249942,0.0007565297,0.0008116409,0.001565324],"category_scores_gemma":[0.008753801,0.0003635705,0.0005692065,0.001185529,0.0005113971,0.002060741,0.001063395,0.0006748195,0.0001546715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001527034,"about_ca_system_score_gemma":0.001161984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01044349,"about_ca_topic_score_gemma":0.01194944,"domain_scores_codex":[0.9989339,0.0005858832,0.00004555222,0.0001453236,0.0002222655,0.00006711936],"domain_scores_gemma":[0.9965299,0.002540941,0.0003513263,0.00007568542,0.000425097,0.0000772303],"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":[0.00005975105,0.00004866782,0.003930702,0.0001339516,0.00009694688,0.00006309983,0.0001494874,0.9368713,0.000832974,0.0180282,0.0004985424,0.03928628],"study_design_scores_gemma":[0.000006135361,0.00002992351,0.0008098808,0.00003719945,0.00003922497,0.00001296747,0.00005163091,0.9807839,0.0002714718,0.01740657,0.0005377728,0.00001318391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1132571,0.001005683,0.8738179,0.00108272,0.000040856,0.00008808711,0.0003322098,0.0001879779,0.01018745],"genre_scores_gemma":[0.9293777,0.0008111422,0.0684536,0.00008667322,0.00002434974,0.00009393522,0.0001742897,0.00001367064,0.0009645022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01044349,"threshold_uncertainty_score":0.02076536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005943438694334852,"score_gpt":0.2181971871190911,"score_spread":0.2122537484247563,"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."}}