{"id":"W2318264551","doi":"10.1080/15732479.2015.1053093","title":"A fuzzy Bayesian belief network for safety assessment of oil and gas pipelines","year":2015,"lang":"en","type":"article","venue":"Structure and Infrastructure Engineering","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian network; Pipeline (software); Vagueness; Risk analysis (engineering); Pipeline transport; Randomness; Fuzzy logic; Reliability engineering; Engineering; Computer science; Bayesian probability; Artificial intelligence; Business; Mathematics","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":[0.0008152304,0.0002730266,0.0006121041,0.0002054281,0.0001253821,0.0001118502,0.0002821535,0.0001740443,0.00003330317],"category_scores_gemma":[0.000632265,0.0001936298,0.0001107213,0.0005655361,0.00008252298,0.0002381715,0.0001229415,0.0002164803,2.093002e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002670249,"about_ca_system_score_gemma":0.00008002024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001569698,"about_ca_topic_score_gemma":0.00003131807,"domain_scores_codex":[0.9979151,0.00004520026,0.0006906819,0.0004419593,0.0005802575,0.0003267901],"domain_scores_gemma":[0.998456,0.0003972813,0.0002175275,0.0003719905,0.0003011549,0.0002560462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001176877,0.000006816886,0.02939581,0.0001001065,0.0001876734,0.000003974956,0.001075143,0.4816487,0.001309699,0.01949239,0.005614038,0.4610479],"study_design_scores_gemma":[0.002519979,0.0003342273,0.1448044,0.0001105574,0.000265733,0.000106011,0.0009457765,0.3198943,0.0003420234,0.429425,0.1003324,0.0009196569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4663,0.006093965,0.5159225,0.003304338,0.002698589,0.000415155,0.0006182696,0.0001618672,0.004485315],"genre_scores_gemma":[0.887752,0.0003579733,0.1110424,0.00007787417,0.0005852942,0.000004581857,0.00002636796,0.00002214732,0.0001313816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4601282,"threshold_uncertainty_score":0.7895994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01652806712623386,"score_gpt":0.2989965168783576,"score_spread":0.2824684497521237,"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."}}