{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00199856,0.0007531834,0.0008562644,0.001729519,0.000579428,0.001311579,0.00151833,0.001435629,0.001892611],"category_scores_gemma":[0.006130586,0.0006073464,0.000935749,0.001167753,0.0006818761,0.002273947,0.0008382063,0.001033214,0.0002540413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002228869,"about_ca_system_score_gemma":0.001591121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02593559,"about_ca_topic_score_gemma":0.01357224,"domain_scores_codex":[0.9988858,0.0004395331,0.00005328894,0.0002278887,0.0002873718,0.0001061423],"domain_scores_gemma":[0.9986644,0.0007970277,0.0001471595,0.00003647842,0.0003027242,0.00005218988],"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":[0.00006839654,0.00003634588,0.001473877,0.00007212425,0.00005934913,0.0000748548,0.00008427284,0.9508491,0.0006375778,0.01733736,0.0006563407,0.02865045],"study_design_scores_gemma":[0.00000535637,0.00001088545,0.0002200492,0.00001067377,0.00001529185,0.0000136893,0.00001056786,0.9928884,0.0001186077,0.006467154,0.0002308431,0.000008498258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02668228,0.000499528,0.9692573,0.0003553184,0.00002961197,0.00004968003,0.0001762164,0.0001131814,0.002836854],"genre_scores_gemma":[0.8801156,0.001056913,0.1148749,0.0001206622,0.00007827984,0.0002210935,0.000405888,0.00001969886,0.003106971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02593559,"threshold_uncertainty_score":0.05156928,"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."}}