{"id":"W2761971436","doi":"10.2495/safe-v7-n2-103-112","title":"Statistical analysis of failure consequences for oil and gas pipelines","year":2017,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline transport; Statistical analysis; Forensic engineering; Environmental science; Petroleum engineering; Engineering; Statistics; Environmental engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002573315,0.00009512292,0.000275433,0.0001689102,0.00003675774,0.00005795298,0.0001755965,0.00005523619,0.00000677955],"category_scores_gemma":[0.0005480915,0.0000861076,0.00007737344,0.00003432369,0.00008448496,0.0001313078,0.00003004124,0.0001229141,6.228125e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001955273,"about_ca_system_score_gemma":0.00001112161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001235766,"about_ca_topic_score_gemma":0.0000221021,"domain_scores_codex":[0.9993327,0.000003842277,0.0003507326,0.00007108425,0.0001503944,0.0000912481],"domain_scores_gemma":[0.999195,0.000333288,0.0001013443,0.00008419045,0.0002143122,0.00007185547],"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":[0.0002057919,0.00008305466,0.01272153,0.0008691633,0.005721607,0.00008065835,0.001233732,0.8890601,0.007247356,0.03580708,0.0001829346,0.04678703],"study_design_scores_gemma":[0.001099754,0.00009517428,0.04596614,0.0003188651,0.0006594812,0.00008295759,0.0001040522,0.9388856,0.001970753,0.00194769,0.008560649,0.0003089177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8628311,0.001637057,0.1324801,0.001230538,0.00118436,0.00004115226,0.0004449851,0.00002963168,0.0001210691],"genre_scores_gemma":[0.9911139,0.003189266,0.00555142,0.000004471151,0.0001212966,0.000001051772,0.000007308101,0.000008210917,0.000003089319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1282828,"threshold_uncertainty_score":0.3511366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005946409513554729,"score_gpt":0.2442099865619895,"score_spread":0.2382635770484348,"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."}}