{"id":"W3106067751","doi":"10.1016/j.ress.2020.107320","title":"Quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian belief network model","year":2020,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"European Research Council","keywords":"Downtime; Resilience (materials science); Bayesian network; Context (archaeology); Vulnerability (computing); Computer science; Probabilistic logic; Risk analysis (engineering); Natural hazard; Hazard; Critical infrastructure; Event (particle physics); Natural disaster; Emergency management; Risk management; Reliability engineering; Engineering; Computer security; Business; Geography; 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":[0.001222093,0.0004377016,0.0003741903,0.00108545,0.0003505509,0.0008196645,0.0006922916,0.0009217348,0.001096338],"category_scores_gemma":[0.005119059,0.0003495992,0.0004849677,0.0006698457,0.0004300749,0.001476795,0.0005185928,0.0006178062,0.0001369279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083303,"about_ca_system_score_gemma":0.0005663499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01822778,"about_ca_topic_score_gemma":0.01507184,"domain_scores_codex":[0.9997208,0.00005170145,0.00001689592,0.00008303821,0.00007014439,0.00005738668],"domain_scores_gemma":[0.9978353,0.001262086,0.0004372036,0.00008100158,0.0002850796,0.00009931282],"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":[0.00009534183,0.00002277275,0.007660318,0.00001799696,0.00003181577,0.00004634017,0.00003600451,0.9828562,0.000690621,0.002405423,0.0001349194,0.006002165],"study_design_scores_gemma":[0.00000130473,0.0000124989,0.002064449,0.000003054161,0.00001064758,0.000008304709,0.0000129258,0.9965629,0.0001959729,0.001085291,0.00003682317,0.000005786446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7597321,0.0002628912,0.2370261,0.0002373518,0.00002531142,0.00002920838,0.0003695593,0.0002063175,0.002111233],"genre_scores_gemma":[0.9955437,0.00004408737,0.003793844,0.000006941889,0.000003880833,0.000007211981,0.0001403425,0.00000627072,0.0004536501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01822778,"threshold_uncertainty_score":0.03624338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009800420136516106,"score_gpt":0.2101321909236212,"score_spread":0.2003317707871051,"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."}}