{"id":"W2315546885","doi":"10.1115/icone16-48127","title":"Treatment of Epistemic and Aleatory Uncertainties in the Statistical Analysis of the Neutronic Protection System in CANDU Reactors","year":2008,"lang":"en","type":"article","venue":"Volume 2: Fuel Cycle and High Level Waste Management; Computational Fluid Dynamics, Neutronics Methods and Coupled Codes; Student Paper Competition","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Power Generation; Bruce Power (Canada); Public Safety Canada; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; University Network of Excellence in Nuclear Engineering; Bruce Power","keywords":"Probabilistic logic; Setpoint; Uncertainty quantification; Reliability engineering; Consistency (knowledge bases); Neutron flux; Reliability (semiconductor); Nuclear engineering; Computer science; Power (physics); Engineering; Neutron; Physics; Nuclear physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002655028,0.0001936247,0.0003975817,0.0002418961,0.00009132639,0.00003312366,0.0001071799,0.00004772437,0.000001881873],"category_scores_gemma":[0.00000248182,0.0001552912,0.0000592036,0.0003995128,0.000105962,0.00008982296,0.00006062604,0.0001151933,1.262018e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002662428,"about_ca_system_score_gemma":0.00001493494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008693755,"about_ca_topic_score_gemma":0.0007603238,"domain_scores_codex":[0.9987823,0.0001449734,0.0004243575,0.0002333919,0.000232845,0.0001821035],"domain_scores_gemma":[0.9995366,0.0001418533,0.00008405536,0.0001661053,0.00004006544,0.00003133264],"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":[0.0000334366,0.0001321144,0.01357255,0.000512854,0.0005889636,0.000006789262,0.001342075,0.695518,0.0005241605,0.2842896,7.88931e-7,0.003478681],"study_design_scores_gemma":[0.0005307373,0.00006207051,0.3269592,0.00004158418,0.0001717368,0.000004611318,0.001370439,0.6701462,0.000001664954,0.0005790152,0.00003482712,0.00009786761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9509776,0.0006040771,0.0475763,0.00005860229,0.00009864824,0.0004344878,0.00007904418,0.0000218859,0.0001493905],"genre_scores_gemma":[0.9969189,0.001173158,0.001742007,0.000006561952,0.00001371996,0.00005089909,0.00006200944,0.00001889179,0.00001386316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3133866,"threshold_uncertainty_score":0.633259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01210491354211313,"score_gpt":0.237595280034195,"score_spread":0.2254903664920818,"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."}}