{"id":"W1810381759","doi":"10.1007/3-540-44842-x_7","title":"Mesh Partitioners for Computational Grids: A Comparison","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Grid; Distributed computing; Metric (unit); Load balancing (electrical power); Parallel computing; Performance metric; Grid computing; Execution time; Mesh generation; Computational science; Finite element method","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001235875,0.0006255739,0.0008102586,0.0006299739,0.000453957,0.0008266713,0.002736157,0.0003470891,0.0000140153],"category_scores_gemma":[0.0001038146,0.0006164371,0.0002598269,0.0006195667,0.0004907887,0.0004326934,0.0004769428,0.0005859227,0.0000566931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003416672,"about_ca_system_score_gemma":0.0006315843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000879526,"about_ca_topic_score_gemma":0.00001570938,"domain_scores_codex":[0.9953625,0.00006421946,0.0009259902,0.001699014,0.001090228,0.0008580509],"domain_scores_gemma":[0.9968361,0.0008588422,0.0005173141,0.001038329,0.0005028229,0.0002465497],"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":[0.000008830843,0.00006976756,0.00008116127,0.00009503133,0.0000347447,0.00002231574,0.0005396182,0.7744402,0.000008713282,0.1471978,0.002561458,0.07494034],"study_design_scores_gemma":[0.0004723288,0.000185432,0.00003028335,0.000293242,0.00001037681,0.0000560463,2.435769e-7,0.7639837,0.00006127616,0.2099979,0.02422616,0.0006829887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002219386,0.0004505619,0.9914632,0.001149498,0.00311602,0.00077211,0.00004010497,0.000253707,0.002732574],"genre_scores_gemma":[0.2380418,0.0000130142,0.756888,0.003142989,0.0008369865,0.00006470198,0.0001543347,0.00006962371,0.0007884758],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2380196,"threshold_uncertainty_score":0.9996287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02634340824033617,"score_gpt":0.2739225565315668,"score_spread":0.2475791482912307,"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."}}