{"id":"W2990993108","doi":"10.1177/1748006x19888421","title":"AK-PDF: An active learning method combining kriging and probability density function for efficient reliability analysis","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Kriging; Probability density function; Function (biology); Monte Carlo method; Reliability (semiconductor); Computer science; Mathematical optimization; Algorithm; Limit (mathematics); Mathematics; Statistics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.01624122,0.0002113338,0.0008849674,0.0002602194,0.0002251917,0.00005050091,0.0003986659,0.0001948915,0.00001493139],"category_scores_gemma":[0.01998561,0.000129275,0.0004185633,0.0009336619,0.0002812984,0.0003946583,0.0001505525,0.0005964452,4.57002e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009679687,"about_ca_system_score_gemma":0.000101157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001936125,"about_ca_topic_score_gemma":8.506676e-7,"domain_scores_codex":[0.9968614,0.0001811389,0.001339426,0.0005032259,0.0008906625,0.0002241238],"domain_scores_gemma":[0.9939778,0.002074867,0.00137221,0.0002824352,0.00209899,0.000193635],"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":[0.001042339,0.0002816837,0.02501117,0.0002391362,0.0001761965,1.008006e-7,0.0007325998,0.9551435,0.003256248,0.009917621,0.00001615049,0.004183288],"study_design_scores_gemma":[0.001663582,0.001197449,0.04455343,0.0001944777,0.00140787,0.00001948557,0.001991523,0.8696067,0.01246922,0.06628392,0.0003015213,0.0003108562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.649055,0.00003724363,0.3501046,0.00006723905,0.0003480979,0.0003391228,0.000007447669,0.0000122912,0.00002890742],"genre_scores_gemma":[0.9641581,0.00003191422,0.03573943,0.000005456937,0.00003969131,0.000004591951,7.469704e-7,0.000006903954,0.00001320248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.315103,"threshold_uncertainty_score":0.9882694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.022115700084773,"score_gpt":0.2855558154777289,"score_spread":0.2634401153929559,"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."}}