{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001440752,0.0002096255,0.0002283294,0.0001063091,0.00006366472,0.00005714942,0.0001759049,0.0001197491,0.00003601154],"category_scores_gemma":[0.00001064156,0.0001839691,0.00007817936,0.0004604717,0.00004520921,0.0002084211,0.00005176216,0.0003101345,0.000003666118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001036487,"about_ca_system_score_gemma":0.00004701195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004140068,"about_ca_topic_score_gemma":0.000004929459,"domain_scores_codex":[0.9988202,0.00001258337,0.0003144194,0.0002389742,0.0001731833,0.0004406574],"domain_scores_gemma":[0.9996101,0.00003135,0.00002326634,0.0002390703,0.00003170428,0.00006454605],"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":[0.000002604114,0.000001723788,0.0004790349,0.0003879612,0.00003903077,0.000009366693,0.0004215357,0.9650773,0.02490783,0.001056767,0.001436065,0.006180819],"study_design_scores_gemma":[0.00007678434,0.00001533845,0.00272877,0.0003692477,0.00002958402,0.00006692349,0.000139492,0.9614135,0.03148357,0.001263079,0.002128807,0.0002849139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1548934,0.0009579457,0.8329266,0.000005703798,0.001602613,0.0001215259,0.000004218682,0.0004031952,0.009084808],"genre_scores_gemma":[0.9524398,0.00003660183,0.04647719,0.00002744475,0.0008683698,0.000003860172,0.000003895344,0.0000495142,0.0000933558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7975463,"threshold_uncertainty_score":0.7502042,"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."}}