{"id":"W2124007553","doi":"10.1109/tmag.2004.824713","title":"Optimal Discretization-Based Load Balancing for Parallel Adaptive Finite-Element Electromagnetic Analysis","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Finite element method; Discretization; Computer science; Load balancing (electrical power); Domain decomposition methods; Benchmark (surveying); Degrees of freedom (physics and chemistry); Mathematical optimization; Mixed finite element method; Applied mathematics; Mathematics; Mathematical analysis; Geometry; Physics","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":[0.0007560062,0.0003822788,0.0003131599,0.0003435282,0.0003013219,0.0004090647,0.0006897785,0.000397074,0.001114915],"category_scores_gemma":[0.002363352,0.0001708961,0.0001867879,0.0003228502,0.0005343288,0.0006993552,0.0007891335,0.0004136681,0.0002365549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003702485,"about_ca_system_score_gemma":0.0004604731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009791888,"about_ca_topic_score_gemma":0.001532611,"domain_scores_codex":[0.9996294,0.0001262745,0.00002191011,0.00003045802,0.0001681854,0.00002380735],"domain_scores_gemma":[0.999562,0.0001769067,0.00004539913,0.00008943921,0.0001076089,0.0000187036],"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":[0.000171331,0.00008307828,0.0009577745,0.00006800683,0.00001935407,0.00005631563,0.0001294875,0.8011551,0.05309569,0.03519165,0.001054218,0.1080179],"study_design_scores_gemma":[0.000008377576,0.00001319931,0.00005853147,0.000002526037,0.000001611074,0.000009483383,0.000007035418,0.9924755,0.003329513,0.003513441,0.0005771269,0.000003798742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02267671,0.00006883712,0.9747167,0.00006242125,0.00001910931,0.00002460402,0.000007737583,0.0001560196,0.00226781],"genre_scores_gemma":[0.627997,0.0001204307,0.3697516,0.00006450241,0.0000244302,0.0001148677,0.00005032271,0.0001269988,0.001749887],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001114915,"threshold_uncertainty_score":0.00399822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255045206945401,"score_gpt":0.2536963031935356,"score_spread":0.2411458511240816,"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."}}