{"id":"W2126244298","doi":"10.1109/tmag.2006.871434","title":"Efficient load balancing for parallel adaptive finite-element electromagnetics with vector tetrahedra","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Finite element method; Tetrahedron; Computer science; Benchmark (surveying); Discretization; Polygon mesh; Electromagnetics; Load balancing (electrical power); Computational electromagnetics; Applied mathematics; Helmholtz equation; Adaptive mesh refinement; Mathematical optimization; Computational science; Algorithm; Mathematics; Mathematical analysis; Electromagnetic field; Boundary value problem; Geometry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001286837,0.0003382034,0.0002827047,0.0001364158,0.0001539399,0.0000485363,0.0001352902,0.0001229296,0.0002045282],"category_scores_gemma":[0.000008518598,0.0003217343,0.0001182911,0.0004258125,0.0000664722,0.00002552494,7.276129e-7,0.0002883371,0.00002308254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002108601,"about_ca_system_score_gemma":0.00005738774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004013825,"about_ca_topic_score_gemma":0.00009242366,"domain_scores_codex":[0.9982842,0.00004785349,0.0003814724,0.0003209582,0.0003840792,0.0005813903],"domain_scores_gemma":[0.9988628,0.000542961,0.00004686482,0.0002811742,0.0001439524,0.0001222735],"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":[0.0001666329,0.0001911879,0.000005006093,0.0000318775,0.00003258525,0.000003184374,0.00007255354,0.9729959,0.008981491,0.0001688921,0.000209373,0.01714133],"study_design_scores_gemma":[0.001890374,0.004146549,0.0003952159,0.00002935249,0.000133235,0.000006469566,0.00003597501,0.9631116,0.02700377,0.0001894658,0.002565857,0.0004921313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02534289,0.0003007883,0.9710031,0.00009645448,0.0003289765,0.0006959966,0.00004437988,0.0003710538,0.00181641],"genre_scores_gemma":[0.8467644,0.00003032448,0.1519078,0.00006958122,0.00008883847,0.0001740095,0.000005488115,0.00007607612,0.0008834983],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8214215,"threshold_uncertainty_score":0.9999235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009659966098849556,"score_gpt":0.2268931661239344,"score_spread":0.2172332000250848,"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."}}