{"id":"W4408597844","doi":"10.1016/j.net.2025.103590","title":"Methodology and preliminary verification of generating heterogeneous multigroup microscopic cross-section libraries for neutron transport codes based on OpenMC","year":2025,"lang":"en","type":"article","venue":"Nuclear Engineering and Technology","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Neutron transport; Section (typography); Nuclear engineering; Cross section (physics); Nuclear physics; Neutron; Computer science; Physics; Engineering; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001030074,0.0005380769,0.00039113,0.0005539759,0.0007155249,0.0006903789,0.001667466,0.0004279781,0.004306274],"category_scores_gemma":[0.002206631,0.0003970134,0.0005574264,0.0004395799,0.0004850941,0.0007471794,0.0008783203,0.0008589886,0.0005557803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099203,"about_ca_system_score_gemma":0.002189265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006567976,"about_ca_topic_score_gemma":0.005497082,"domain_scores_codex":[0.9993694,0.0001032751,0.00002883986,0.00007705585,0.0003635781,0.00005779186],"domain_scores_gemma":[0.9987713,0.0003048627,0.00008239511,0.0003270955,0.0004664728,0.00004790721],"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":[0.00006991954,0.00009508158,0.002365017,0.0001800019,0.00003356174,0.0001380461,0.0001560396,0.8776536,0.0238336,0.05114244,0.001715113,0.04261753],"study_design_scores_gemma":[0.00001744873,0.00004043961,0.0003500277,0.00001502927,0.000006181237,0.00003527922,0.00001615527,0.9581842,0.03217516,0.002325457,0.006818066,0.00001653155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07745019,0.0001389029,0.9038439,0.000121219,0.00005543569,0.0002620707,0.0006024545,0.00478392,0.01274196],"genre_scores_gemma":[0.4752235,0.0001344445,0.518422,0.00007294902,0.00001798596,0.0006820863,0.001134116,0.001150694,0.003162248],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006567976,"threshold_uncertainty_score":0.01440597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235219959175304,"score_gpt":0.2318517109055118,"score_spread":0.2194995113137588,"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."}}