{"id":"W2892071527","doi":"10.1177/0361198118795006","title":"Impact of Extreme Events on Transportation Infrastructure in Iowa: A Bayesian Network Approach","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transportation infrastructure; Extreme weather; Critical infrastructure; Bayesian network; Vulnerability (computing); Flooding (psychology); Transport engineering; Identification (biology); Climate change; Computer science; Engineering; Computer security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001905945,0.0005822417,0.0004927529,0.002262586,0.0005243616,0.001659575,0.0009361928,0.001149206,0.001630581],"category_scores_gemma":[0.005524641,0.0007380557,0.0008812366,0.001251335,0.0009143641,0.001341881,0.001138494,0.0008781516,0.0001199348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00274553,"about_ca_system_score_gemma":0.001427535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09348517,"about_ca_topic_score_gemma":0.08356286,"domain_scores_codex":[0.9993067,0.0003499714,0.00002676343,0.0001475524,0.00008355117,0.00008545997],"domain_scores_gemma":[0.9969117,0.002348002,0.0003187939,0.00005739037,0.0002896143,0.00007448714],"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":[0.00004798015,0.00004154348,0.01883034,0.00002582356,0.0001010617,0.0001488909,0.00007770897,0.9667149,0.0002139814,0.007950095,0.0004697571,0.005377869],"study_design_scores_gemma":[0.000007127349,0.00002350862,0.005454211,0.00001951731,0.00005171057,0.00002968865,0.0001610676,0.9857788,0.00008681596,0.007970721,0.0003989815,0.00001771818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7258637,0.0006595497,0.2571167,0.001647338,0.00003768195,0.0001957788,0.001859657,0.000153869,0.01246573],"genre_scores_gemma":[0.9814525,0.0005931511,0.01544075,0.0000514194,0.00001542073,0.00008908265,0.000615387,0.00001121608,0.001731067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09348517,"threshold_uncertainty_score":0.1858821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04646573776358259,"score_gpt":0.3503216760424903,"score_spread":0.3038559382789077,"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."}}