{"id":"W337479017","doi":"","title":"Implementation of generalized coarse-mesh rebalance in NEWTRNX for acceleration of parallel block-jacobi transport","year":2007,"lang":"en","type":"article","venue":"Transactions of the American Nuclear Society","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computational science; Solver; Discretization; Parallel computing; Computer science; Partition (number theory); Massively parallel; Generalized minimal residual method; Neutron transport; Block (permutation group theory); Scaling; Algorithm; Applied mathematics; Mathematical optimization; Residual; Mathematics; Geometry; Mathematical analysis; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001214543,0.00009310039,0.0002329729,0.00003175925,0.0000282124,0.000002402805,0.0001275093,0.0000292429,0.0000158688],"category_scores_gemma":[8.104275e-7,0.00009068,0.0002276492,0.0003194363,0.00007786987,0.00007856769,0.000002214106,0.00009076901,2.01222e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004543868,"about_ca_system_score_gemma":0.00001380338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006032318,"about_ca_topic_score_gemma":0.0001331541,"domain_scores_codex":[0.9993219,0.000006057484,0.0003310468,0.00008335278,0.0001105385,0.0001471006],"domain_scores_gemma":[0.9996278,0.00002539815,0.0001166197,0.0001689231,0.00003833817,0.0000228882],"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":[0.00008431727,0.0001086416,0.001490345,0.0002805599,0.0002323126,1.082683e-7,0.004386815,0.1986369,0.778442,0.0007995495,0.0003079613,0.01523046],"study_design_scores_gemma":[0.007300063,0.000712027,0.3501106,0.0002459816,0.0004782041,0.000006440358,0.01886807,0.3524018,0.2591967,0.0003334545,0.009159486,0.001187214],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.954289,0.0000148971,0.0451973,0.0000354856,0.00007652539,0.0002187403,0.00004548825,0.0000373089,0.00008525341],"genre_scores_gemma":[0.9892426,0.0001213964,0.01055423,0.00001487139,0.00001585822,0.000005115565,0.000003751295,0.0000360231,0.000006195387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5192453,"threshold_uncertainty_score":0.3697823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009047020741637692,"score_gpt":0.2454693882462916,"score_spread":0.236422367504654,"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."}}