{"id":"W4387901098","doi":"10.1016/j.ijcip.2023.100638","title":"Dynamic predictive analysis of the consequences of gas pipeline failures using a Bayesian network","year":2023,"lang":"en","type":"article","venue":"International Journal of Critical Infrastructure Protection","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline (software); Bayesian network; Reliability (semiconductor); Gas pipeline; Dynamic Bayesian network; Bayesian probability; Natural gas; Computer science; Reliability engineering; Risk analysis (engineering); Engineering; Data mining; Operations research; Machine learning; Petroleum engineering; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001909953,0.0001150566,0.0004714613,0.001062509,0.000107496,0.00007738135,0.0008452181,0.0001057558,0.0002956747],"category_scores_gemma":[0.006393391,0.00006852547,0.0006291314,0.003099394,0.000624228,0.000326503,0.0001173657,0.0003290327,0.000001196357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000869097,"about_ca_system_score_gemma":0.0001637484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001136033,"about_ca_topic_score_gemma":0.00007557871,"domain_scores_codex":[0.995765,0.0004129602,0.001360105,0.0001841136,0.002125227,0.0001525595],"domain_scores_gemma":[0.9949371,0.0008541165,0.001019143,0.0002084475,0.002914066,0.00006714366],"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":[0.0003840275,0.00003949046,0.01229934,0.000008209556,0.001355901,0.00001523636,0.0004320544,0.9473997,0.01659485,0.002177551,0.0001655508,0.01912808],"study_design_scores_gemma":[0.0001921413,0.00008998526,0.0623863,0.00007996486,0.0007478847,0.00007219958,0.00073942,0.6358467,0.003203235,0.2964383,0.0001303564,0.00007354012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2948064,0.00008667844,0.6944619,0.009158293,0.00120166,0.0001130033,0.00007421905,0.000009206306,0.00008866292],"genre_scores_gemma":[0.9963064,0.00003135858,0.003414347,0.00004548899,0.0001731045,0.000001548146,0.000002028521,0.000005207554,0.00002057868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7014999,"threshold_uncertainty_score":0.7653948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03704475355990102,"score_gpt":0.3850951610946947,"score_spread":0.3480504075347937,"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."}}