{"id":"W2947886800","doi":"10.48550/arxiv.1905.11808","title":"Bayesian updating for data adjustments and multi-level uncertainty propagation within Total Monte Carlo","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Uppsala Universitet; McMaster University","keywords":"Nuclear data; Monte Carlo method; Benchmark (surveying); Computer science; Bayesian probability; Data assimilation; Data set; Data file; Likelihood function; Algorithm; Data mining; Neutron; Statistics; Database; Physics; Mathematics; Artificial intelligence; Estimation theory; 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":[0.008767614,0.001481858,0.001833961,0.002706509,0.001178282,0.002673238,0.003995985,0.001371103,0.003666479],"category_scores_gemma":[0.02452752,0.001314981,0.001886547,0.002167444,0.001189611,0.003285924,0.003752102,0.002350786,0.001000569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001494205,"about_ca_system_score_gemma":0.002469735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007281526,"about_ca_topic_score_gemma":0.007897067,"domain_scores_codex":[0.9938107,0.002412984,0.0003571389,0.0009377824,0.002219814,0.0002615036],"domain_scores_gemma":[0.9906915,0.004847486,0.0006358802,0.001659405,0.001967632,0.0001981023],"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":[0.0002430586,0.00009188953,0.002275648,0.0001985346,0.0002228144,0.0001080989,0.0003105811,0.6612411,0.004597819,0.04944844,0.001773274,0.2794887],"study_design_scores_gemma":[0.00001800595,0.00003450003,0.0003742852,0.00001815885,0.00003679885,0.00004533331,0.00001759773,0.9709967,0.003065061,0.0224137,0.002946956,0.00003272955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002289488,0.00004349647,0.9965868,0.00002699063,0.00001244556,0.00003242906,0.00003794264,0.0005163751,0.0004540496],"genre_scores_gemma":[0.07976123,0.00008645717,0.9176125,0.00008491149,0.00003512574,0.0001969548,0.0004404865,0.0007022797,0.001080031],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008767614,"threshold_uncertainty_score":0.04636818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1052801989408432,"score_gpt":0.1966376269095204,"score_spread":0.09135742796867717,"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."}}