{"id":"W2124072969","doi":"10.1111/risa.12158","title":"Risk Management of Domino Effects Considering Dynamic Consequence Analysis","year":2013,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Domino effect; Risk analysis (engineering); Domino; Risk assessment; Bayesian network; Risk management; Process (computing); Bayesian probability; Computer science; Reliability engineering; Engineering; Computer security; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003703347,0.001162836,0.0009322345,0.001981605,0.0006810147,0.001968831,0.001521652,0.0007348628,0.00195503],"category_scores_gemma":[0.01196934,0.0004734798,0.001224553,0.0009290967,0.0009767012,0.002141455,0.001912569,0.0009604256,0.00009833786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001603011,"about_ca_system_score_gemma":0.001511026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003537796,"about_ca_topic_score_gemma":0.003040054,"domain_scores_codex":[0.9974765,0.0008790714,0.0001351564,0.0003964657,0.000867494,0.0002452772],"domain_scores_gemma":[0.9920677,0.00518903,0.001016527,0.0003529136,0.001139434,0.0002344295],"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":[0.00009706123,0.00007128828,0.005061001,0.0001077748,0.0001757666,0.0003887996,0.0001689808,0.9108309,0.001681351,0.04349288,0.0004645664,0.03745954],"study_design_scores_gemma":[0.00001338018,0.00008082188,0.001488868,0.00002269396,0.00008776849,0.0001293407,0.00007844576,0.9443093,0.0007253715,0.05228353,0.0007531521,0.00002729085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09098681,0.0004722594,0.8997771,0.0003738752,0.00003167268,0.0001518454,0.0001265993,0.0001126506,0.007967295],"genre_scores_gemma":[0.9395285,0.0003713225,0.05816515,0.00004649796,0.00004556509,0.0001215279,0.0001081197,0.00002473221,0.001588635],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003703347,"threshold_uncertainty_score":0.01958537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01813798068961786,"score_gpt":0.3204601526683761,"score_spread":0.3023221719787582,"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."}}