{"id":"W1940286691","doi":"10.1111/j.1539-6924.2012.01854.x","title":"Domino Effect Analysis Using Bayesian Networks","year":2012,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":267,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Memorial University of Newfoundland","funders":"","keywords":"Domino effect; Domino; Bayesian network; Bayesian probability; Computer science; Probabilistic logic; Machine learning; Bayesian inference; Artificial intelligence; Path analysis (statistics); Data mining; Dynamic Bayesian network; Path (computing); Bayesian statistics","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":[0.004948183,0.001491142,0.001561698,0.004907215,0.0009096191,0.002175888,0.002343416,0.001303884,0.0032726],"category_scores_gemma":[0.01601016,0.000904483,0.001689771,0.003068365,0.001485125,0.003447554,0.001628626,0.001629979,0.0003359202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002086967,"about_ca_system_score_gemma":0.001720073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00886465,"about_ca_topic_score_gemma":0.00520506,"domain_scores_codex":[0.9969332,0.001524848,0.0001341809,0.000417111,0.0008384084,0.0001521975],"domain_scores_gemma":[0.9912596,0.00678101,0.0008092064,0.0003692527,0.0006617471,0.0001191998],"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":[0.00003429723,0.00003273707,0.001958165,0.0001200753,0.0001463464,0.0001170639,0.0001499079,0.8061439,0.001307609,0.1483932,0.0006405545,0.04095609],"study_design_scores_gemma":[0.000007152219,0.00001409092,0.0003581262,0.00002554839,0.0000330008,0.00004809282,0.0000255576,0.9258809,0.0004923146,0.07158681,0.001504809,0.000023521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002919049,0.0001407499,0.9954897,0.00006324711,0.000007755611,0.00003751398,0.00006429489,0.00009013839,0.001187551],"genre_scores_gemma":[0.3826045,0.001463694,0.611087,0.0001099606,0.0000955305,0.0005930943,0.0004390277,0.0001559081,0.003451296],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00886465,"threshold_uncertainty_score":0.02616882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03838190626078282,"score_gpt":0.368584856690397,"score_spread":0.3302029504296142,"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."}}