{"id":"W2050416191","doi":"10.1080/16864360.2014.846097","title":"A Virtual Prognostic Tool for Nuclear Power Electronics Reliability","year":2013,"lang":"en","type":"article","venue":"Computer-Aided Design and Applications","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Reliability engineering; Nuclear power; Electronics; Power electronics; Computer science; Systems engineering; Power (physics); Engineering; Electrical engineering; Physics; Nuclear physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007337221,0.0008709639,0.0006227971,0.001514026,0.000374646,0.001124485,0.001043342,0.0008486838,0.02280921],"category_scores_gemma":[0.00354177,0.0003494621,0.0005494098,0.0008774812,0.0003679214,0.001428726,0.001161038,0.000723579,0.003560335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003429186,"about_ca_system_score_gemma":0.0006674458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001024745,"about_ca_topic_score_gemma":0.001041092,"domain_scores_codex":[0.9997198,0.00005477803,0.00001932309,0.00003349695,0.0001534122,0.00001924618],"domain_scores_gemma":[0.9991814,0.0003889844,0.00008010712,0.0001147561,0.0001955975,0.00003905338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003548613,0.00008713026,0.002312707,0.0004089283,0.00006825641,0.0004129222,0.0002278423,0.3864712,0.01142954,0.02679237,0.03204922,0.5393851],"study_design_scores_gemma":[0.00004345554,0.00009508295,0.000626185,0.00007049636,0.00004370557,0.0002521908,0.00003410184,0.9503492,0.00397745,0.01714427,0.02732652,0.00003728974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01242523,0.0006569723,0.9521185,0.0002967923,0.0003029698,0.00007191491,0.0008937349,0.02392593,0.009307951],"genre_scores_gemma":[0.6177665,0.001235096,0.3606604,0.0002496943,0.0002399657,0.000316426,0.001809356,0.002090412,0.01563218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02280921,"threshold_uncertainty_score":0.07630438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03419845076493652,"score_gpt":0.2767858404319863,"score_spread":0.2425873896670498,"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."}}