{"id":"W2518920558","doi":"10.1016/j.anucene.2016.08.004","title":"Effect of 3-D moderator flow configurations on the reactivity of CANDU nuclear reactors","year":2016,"lang":"en","type":"article","venue":"Annals of Nuclear Energy","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Atomic Energy of Canada Limited; Natural Sciences and Engineering Research Council of Canada; Électricité de France; Korea Atomic Energy Research Institute; Compute Canada","keywords":"Coolant; Moderation; Reactivity (psychology); Nuclear engineering; Nuclear reactor; Flow (mathematics); Computational fluid dynamics; Materials science; Research reactor; Mechanics; Nuclear physics; Physics; Computer science; Neutron","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006089847,0.0005328051,0.0004596234,0.0004550213,0.0005166124,0.000800246,0.0004732871,0.0006328793,0.003088083],"category_scores_gemma":[0.001615707,0.0003741764,0.0002670575,0.0002847454,0.0004483603,0.0004619406,0.0005410394,0.0004235089,0.000332066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003759162,"about_ca_system_score_gemma":0.0003334742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001091265,"about_ca_topic_score_gemma":0.0008428323,"domain_scores_codex":[0.999769,0.00006368185,0.00001238254,0.0000461036,0.00003129974,0.00007742904],"domain_scores_gemma":[0.9988218,0.0007161071,0.0001116417,0.00008928889,0.0001172378,0.0001438363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01541953,0.0005163894,0.03449956,0.000340499,0.0001775357,0.0009708033,0.0003984317,0.25748,0.6557562,0.002410213,0.001326403,0.03070435],"study_design_scores_gemma":[0.0005225999,0.002656117,0.03609009,0.00004568141,0.0002061762,0.0003325121,0.0004661508,0.2036866,0.7528483,0.0008435767,0.002138649,0.0001635076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962973,0.0002262551,0.001021358,0.00004951558,0.00001936035,0.000004020047,0.00009944422,0.0001536129,0.002129113],"genre_scores_gemma":[0.9993801,0.00005058427,0.0003257997,0.00001503959,0.000004463291,0.000002700249,0.00004773031,0.00002725438,0.0001464479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003088083,"threshold_uncertainty_score":0.01033068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488814312815182,"score_gpt":0.2145444678155891,"score_spread":0.1996563246874373,"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."}}