{"id":"W1964690100","doi":"10.1115/omae2009-79470","title":"Hierarchical Modeling of Pipeline Defect Growth Subject to ILI Uncertainty","year":2009,"lang":"en","type":"article","venue":"","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline (software); Sizing; Computer science; Path (computing); Uncertainty analysis; Feature (linguistics); Uncertainty quantification; Reliability engineering; Engineering; Simulation; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001654377,0.0001386766,0.0002735264,0.0001339981,0.00003307325,0.00001308565,0.0001631103,0.0001027854,0.0001909552],"category_scores_gemma":[0.0002006192,0.0001045534,0.0002411909,0.0004037129,0.00002372242,0.00005721336,0.00001605439,0.0002437832,0.00002139274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004649882,"about_ca_system_score_gemma":0.00001025694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003529653,"about_ca_topic_score_gemma":0.0001339193,"domain_scores_codex":[0.9991155,0.00002488277,0.0003036699,0.0001727497,0.0001677775,0.0002153671],"domain_scores_gemma":[0.9994847,0.0000927689,0.00001022963,0.0002059895,0.00009651389,0.0001097511],"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":[0.00002378615,0.00001525198,0.0001171742,0.00002878836,0.00003586635,0.000001191555,0.0001560739,0.9916545,0.00252272,0.001916047,0.0004476131,0.003081005],"study_design_scores_gemma":[0.0001101959,0.00006351958,0.0003124395,0.00001895851,0.00003745038,0.000002343551,0.0000577553,0.9850624,0.005641351,0.008475739,0.00006250969,0.0001553033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8594167,0.0001079772,0.1297807,0.0008593304,0.00009083343,0.0001095858,0.000006431858,0.0002365556,0.009391798],"genre_scores_gemma":[0.9960871,0.00003297491,0.003483134,0.000231279,0.00005436633,0.000001722383,0.000007128759,0.000007906337,0.00009437405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1366704,"threshold_uncertainty_score":0.4263566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01241696573078766,"score_gpt":0.2364354431273774,"score_spread":0.2240184773965897,"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."}}