{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003130669,0.0007031334,0.001226579,0.00112642,0.0004430673,0.001272658,0.002092605,0.001291759,0.002245642],"category_scores_gemma":[0.008240554,0.001046668,0.001154126,0.001119231,0.001342244,0.001734527,0.001227302,0.001114422,0.0003159296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002114351,"about_ca_system_score_gemma":0.001136188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03482431,"about_ca_topic_score_gemma":0.02110744,"domain_scores_codex":[0.9988587,0.0003351232,0.00005676537,0.0002998278,0.0002663638,0.0001831912],"domain_scores_gemma":[0.9947996,0.003199492,0.0009769114,0.0002887929,0.0005872556,0.0001478694],"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":[0.00002326212,0.00000801951,0.0008999311,0.00001998932,0.00001812722,0.00006733461,0.00004865376,0.9819028,0.0004029701,0.01318432,0.0002388369,0.003185768],"study_design_scores_gemma":[0.000002129858,0.000003826465,0.0002205489,0.000001277914,0.00000373434,0.000006435056,0.000004463993,0.9965296,0.0000383216,0.003126738,0.00005888395,0.000003939874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0964831,0.0003824087,0.8978666,0.0004125822,0.00002701129,0.00006504424,0.000705961,0.0004645167,0.003592759],"genre_scores_gemma":[0.9501492,0.0003506951,0.04224601,0.00008504054,0.00004326161,0.0001273952,0.0005577502,0.0000933035,0.006347307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03482431,"threshold_uncertainty_score":0.06924325,"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."}}