{"id":"W3048590680","doi":"10.1016/j.pnucene.2020.103443","title":"Nuclear data uncertainty propagation and modeling uncertainty impact evaluation in neutronics core simulation","year":2020,"lang":"en","type":"article","venue":"Progress in Nuclear Energy","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Nuclear Laboratories; Nuclear Energy University Program; U.S. Department of Energy","keywords":"Uncertainty quantification; Uncertainty analysis; Propagation of uncertainty; Nuclear data; Neutron transport; Computer science; Covariance; Sensitivity analysis; Covariance matrix; Algorithm; Physics; Nuclear physics; Statistics; Simulation; Neutron; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002125435,0.0001908748,0.0002039308,0.0000931855,0.00003530311,0.0000797391,0.0002195307,0.00009704763,0.00002288641],"category_scores_gemma":[0.00003440554,0.0001985797,0.00002483613,0.0003256053,0.00002348845,0.0004226907,0.0001323495,0.0002281543,0.000003849184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002205576,"about_ca_system_score_gemma":0.00002097487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001384978,"about_ca_topic_score_gemma":0.00004774526,"domain_scores_codex":[0.9988613,0.00002983181,0.0002723609,0.0003228808,0.0002401456,0.0002734408],"domain_scores_gemma":[0.9995233,0.00002186355,0.00003622546,0.0002820216,0.00004274312,0.0000938755],"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":[0.00003274481,0.0000176659,0.0002847243,0.00005083286,0.00001485342,0.000002630909,0.0004697226,0.9402815,0.0003047724,0.001903142,0.00001492022,0.05662248],"study_design_scores_gemma":[0.0004982986,0.00004006986,0.0004267345,0.00005994719,0.00001227846,8.589382e-7,0.0001003653,0.9970715,0.000002140607,0.0002347003,0.001341566,0.0002115359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975317,0.000715557,0.0007260974,0.00008595539,0.00009081248,0.0002273487,0.00001655084,0.00025639,0.0003495837],"genre_scores_gemma":[0.9992908,0.0001218726,0.0002290815,0.00003013193,0.00009620505,0.000007392629,0.0001302696,0.00009400182,2.456498e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05678999,"threshold_uncertainty_score":0.8097844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06493220161469548,"score_gpt":0.299044242403306,"score_spread":0.2341120407886105,"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."}}