{"id":"W2900357607","doi":"10.1115/ipc2018-78413","title":"Analysis of the National Energy Board Pipeline Integrity Performance Measures Data","year":2018,"lang":"en","type":"article","venue":"","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Energy Regulator","funders":"","keywords":"Lagging; Pipeline (software); Pipeline transport; Integrity management; Risk analysis (engineering); Computer science; Stakeholder; Business; Engineering; Accounting","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":[0.008987455,0.0004482639,0.0004448949,0.01045478,0.000534311,0.001870283,0.000760247,0.000444236,0.003416211],"category_scores_gemma":[0.02502247,0.0003269428,0.0005520733,0.01780817,0.0003441981,0.001063027,0.00106953,0.0009130829,0.00191121],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004383408,"about_ca_system_score_gemma":0.004302018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07226177,"about_ca_topic_score_gemma":0.05776577,"domain_scores_codex":[0.9865538,0.001452899,0.001366832,0.0009064166,0.008861328,0.0008588005],"domain_scores_gemma":[0.9440563,0.00925123,0.01039288,0.002759568,0.0326993,0.0008407295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000536213,0.0005775694,0.7100605,0.000997214,0.0002669486,0.0004893113,0.003421749,0.01005587,0.005931606,0.005336,0.0774776,0.1848495],"study_design_scores_gemma":[0.00001164791,0.0002069554,0.8993587,0.0001298335,0.00002942061,0.00008429311,0.001531349,0.002921822,0.003391017,0.0001883119,0.09210685,0.00003994428],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6178905,0.001097291,0.01432654,0.001608109,0.0001766645,0.001396531,0.2724807,0.001111302,0.08991241],"genre_scores_gemma":[0.6200881,0.0008754454,0.01861825,0.0003375016,0.00007546444,0.00177341,0.3312062,0.0003298963,0.02669582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9956166,"threshold_uncertainty_score":0.1436823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04636406223861645,"score_gpt":0.2695807002887608,"score_spread":0.2232166380501444,"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."}}