{"id":"W2905061194","doi":"","title":"Quantification of Uncertainties in Inline Inspection Data for Metal-loss Corrosion on Energy Pipelines and Implications for Reliability Analysis","year":2018,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pipeline transport; Reliability (semiconductor); Corrosion; Reliability engineering; Forensic engineering; Environmental science; Computer science; Engineering; Metallurgy; Materials science; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007065077,0.0007225234,0.0004942738,0.002558101,0.0004461326,0.001458156,0.001254614,0.0008741012,0.0002847912],"category_scores_gemma":[0.02915423,0.0003584563,0.0005241603,0.002475709,0.0006998616,0.001282324,0.0007747347,0.0007409836,0.00008579784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003267541,"about_ca_system_score_gemma":0.001781498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04404378,"about_ca_topic_score_gemma":0.03790399,"domain_scores_codex":[0.9953046,0.00124525,0.0002429757,0.0007249067,0.002272805,0.0002094458],"domain_scores_gemma":[0.9753542,0.01344936,0.004372787,0.002234381,0.004416991,0.0001722707],"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":[0.0002363947,0.0002305779,0.2338098,0.0003449098,0.0002770525,0.0006198938,0.0008391135,0.6661758,0.01462427,0.008333167,0.00126837,0.07324062],"study_design_scores_gemma":[0.000008819681,0.0001278355,0.1806954,0.00008975688,0.00006188929,0.000237731,0.0005017677,0.7994846,0.0120923,0.00496105,0.001655091,0.00008367617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8028432,0.0004871223,0.1907601,0.0004477963,0.00002501337,0.0001080801,0.002570398,0.0003553001,0.002403122],"genre_scores_gemma":[0.9773206,0.0001995261,0.02066319,0.0000408084,0.00001557374,0.00003274306,0.001461359,0.00001973483,0.0002465631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04404378,"threshold_uncertainty_score":0.08757484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1308006549082259,"score_gpt":0.3512912873985352,"score_spread":0.2204906324903093,"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."}}