{"id":"W4393291811","doi":"10.1061/ajrua6.rueng-1034","title":"Metric Systems for Performance Evaluation of Active Learning Kriging Configurations for Reliability Analysis","year":2024,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Metric (unit); Kriging; Computer science; Reliability (semiconductor); Reliability engineering; Machine learning; Artificial intelligence; Engineering; Operations management; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01057776,0.001677223,0.001162911,0.003557901,0.0005161918,0.001072162,0.00125333,0.0008131353,0.001235882],"category_scores_gemma":[0.03035024,0.0003421611,0.0006669962,0.002899335,0.0007385869,0.001575134,0.001273475,0.0009490597,0.0003619506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067012,"about_ca_system_score_gemma":0.0008988185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001799706,"about_ca_topic_score_gemma":0.00180934,"domain_scores_codex":[0.9924868,0.004431994,0.0005046006,0.0004865843,0.001909349,0.0001806282],"domain_scores_gemma":[0.9875251,0.006566879,0.001315888,0.001621312,0.00283048,0.0001403543],"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":[0.0004746362,0.0001951794,0.007951265,0.0003924875,0.0001974632,0.0000741028,0.000230779,0.8098543,0.009706977,0.0155445,0.001121428,0.154257],"study_design_scores_gemma":[0.00002173751,0.0006524766,0.003132261,0.00003893159,0.0000308266,0.00006384807,0.0000729499,0.9815472,0.009324591,0.004017107,0.001042514,0.00005556196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1627358,0.0006042683,0.8301076,0.00009566512,0.00005087136,0.0003401113,0.0003820355,0.001438474,0.004245106],"genre_scores_gemma":[0.7552655,0.0001557096,0.243147,0.00002691872,0.00001072303,0.0003857226,0.0004757259,0.0001158657,0.0004168192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01057776,"threshold_uncertainty_score":0.05594116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0416758247615118,"score_gpt":0.3151130710443866,"score_spread":0.2734372462828748,"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."}}