{"id":"W2747743000","doi":"10.1016/j.ast.2017.08.011","title":"Zone-based reliability analysis on fatigue life of GH720Li turbine disk concerning uncertainty quantification","year":2017,"lang":"en","type":"article","venue":"Aerospace Science and Technology","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Reliability (semiconductor); Turbine; Extrapolation; Probabilistic logic; Uncertainty quantification; Reliability engineering; Computer science; Empirical probability; Finite element method; Discretization; Bayesian inference; Bayesian probability; Engineering; Mathematics; Structural engineering; Statistics; Mechanical engineering; Artificial intelligence; Posterior probability; Machine learning; Power (physics); Physics","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":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.004158985,0.0001488041,0.0004319933,0.0008136421,0.0008120739,0.0002200752,0.001702601,0.0001531644,0.0000233868],"category_scores_gemma":[0.03005461,0.000103822,0.00006871855,0.002927515,0.004100678,0.0002914916,0.0002116696,0.0001937788,0.00001018625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005842837,"about_ca_system_score_gemma":0.0003118805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001548446,"about_ca_topic_score_gemma":0.00005627089,"domain_scores_codex":[0.9973459,0.00004523202,0.0004409834,0.0008265421,0.001031033,0.0003103019],"domain_scores_gemma":[0.9957222,0.0005918298,0.0005290105,0.002085784,0.0009438624,0.000127313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000128897,0.0003078803,0.4330573,0.00003776074,0.0001063784,0.000007366838,0.0004718186,0.3421667,0.04092311,0.1455941,0.001104523,0.03609418],"study_design_scores_gemma":[0.0009381927,0.0005896605,0.2477897,0.00007220264,0.0001924536,0.000002099691,0.001195442,0.7050848,0.02617365,0.01600569,0.001403019,0.0005530708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7923734,0.00007587653,0.1973848,0.009338401,0.000205731,0.0002048505,0.00001316923,0.0000963224,0.0003074364],"genre_scores_gemma":[0.996649,0.000009632217,0.003111078,0.00004262869,0.00001026275,0.00001323923,0.000001344682,0.000004470469,0.0001583252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3629181,"threshold_uncertainty_score":0.9986096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0931616243856528,"score_gpt":0.3600443351542431,"score_spread":0.2668827107685903,"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."}}