{"id":"W3183474108","doi":"10.1016/j.ijdrr.2021.102491","title":"Quantifying restoration time of pipelines after earthquakes: Comparison of Bayesian belief networks and fuzzy models","year":2021,"lang":"en","type":"article","venue":"International Journal of Disaster Risk Reduction","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"European Research Council","keywords":"Downtime; Bayesian network; Computer science; Resilience (materials science); Fuzzy logic; Contingency plan; Risk analysis (engineering); Reliability engineering; Engineering; Computer security; Artificial intelligence; Business","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":[0.004560335,0.0006396875,0.0006758458,0.002193437,0.0003987531,0.001669783,0.001083927,0.00153964,0.001102567],"category_scores_gemma":[0.01440214,0.0004205031,0.0009730639,0.001162999,0.0005627837,0.002954417,0.0006868885,0.0007239273,0.00009968933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001833076,"about_ca_system_score_gemma":0.001183994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02071562,"about_ca_topic_score_gemma":0.01319364,"domain_scores_codex":[0.9993482,0.0002352345,0.00005159276,0.0001362386,0.000144548,0.00008421946],"domain_scores_gemma":[0.9900366,0.007797012,0.001106092,0.0002462043,0.0006475444,0.0001664822],"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":[0.0001725296,0.00004192387,0.00397066,0.00004112487,0.00005645537,0.00002194481,0.00005770957,0.9758987,0.0003043226,0.004303537,0.0001193453,0.01501175],"study_design_scores_gemma":[0.000004013338,0.00002500395,0.001229139,0.00001030052,0.00002701669,0.000008632996,0.00002828135,0.9948414,0.0001700312,0.003595348,0.00005159323,0.000009313632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5874887,0.0009381628,0.4074728,0.0005857579,0.00005035441,0.00005274748,0.0004041457,0.0001727069,0.002834702],"genre_scores_gemma":[0.9859638,0.0002110311,0.01333823,0.00001566325,0.00001210339,0.00001450931,0.0001157176,0.00000970108,0.0003191154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02071562,"threshold_uncertainty_score":0.04119009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0130710528362164,"score_gpt":0.2687959815977958,"score_spread":0.2557249287615794,"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."}}