{"id":"W4403148220","doi":"10.1016/j.anucene.2024.110930","title":"Framework for the correct treatment of model input parameters for Bayesian updating problems in nuclear engineering","year":2024,"lang":"en","type":"article","venue":"Annals of Nuclear Energy","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Bayesian probability; Computer science; Applied mathematics; Algorithm; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008064036,0.0001566876,0.000345981,0.0002128403,0.00004992056,0.00007650895,0.000385842,0.0001065765,0.00001585407],"category_scores_gemma":[0.001211305,0.0001001585,0.0002519231,0.0003979444,0.00005180525,0.0001077557,0.0000364034,0.00006255942,0.000001980123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002504812,"about_ca_system_score_gemma":0.00004839729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005357726,"about_ca_topic_score_gemma":0.000004995628,"domain_scores_codex":[0.9985558,0.00002576456,0.0005632426,0.0003114238,0.0002777685,0.0002659902],"domain_scores_gemma":[0.996235,0.003117405,0.0001103772,0.0003695598,0.000114166,0.00005353914],"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":[0.00002871362,0.00004495265,0.000003162957,0.00004403598,0.00004993744,5.6904e-7,0.0006753862,0.7604141,0.0002808452,0.2093376,0.001328616,0.027792],"study_design_scores_gemma":[0.000119992,0.0003052918,0.00001301618,0.0001634983,0.00001673166,0.000001237572,0.0001581522,0.9297405,0.0002903601,0.04737078,0.02171611,0.0001042749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005340669,0.0007866857,0.9918327,0.001085419,0.0003537196,0.0003369326,0.00004217587,0.00006294547,0.0001587847],"genre_scores_gemma":[0.9177429,0.0001726196,0.0817515,0.0001056813,0.00004762204,0.00005114231,0.000002008135,0.00004941739,0.00007708603],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9124023,"threshold_uncertainty_score":0.4084343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1870606143175658,"score_gpt":0.365199164710545,"score_spread":0.1781385503929792,"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."}}