{"id":"W1984785310","doi":"10.1145/369534.369537","title":"Parallel shared-memory simulator performance for large ATM networks","year":2000,"lang":"en","type":"article","venue":"ACM Transactions on Modeling and Computer Simulation","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Parallel computing; Speedup; Kernel (algebra); Benchmark (surveying); Multiprocessing; Shared memory; Performance improvement","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":[],"consensus_categories":[],"category_scores_codex":[0.0002405914,0.0002167543,0.0001977634,0.0001251916,0.0005982954,0.0002111015,0.0004698747,0.0001316407,0.00001643657],"category_scores_gemma":[0.000004316874,0.0002220528,0.00009887361,0.0002157904,0.00001199848,0.0005454774,0.00001803871,0.000179686,0.000008384476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002897638,"about_ca_system_score_gemma":0.00002165335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004278703,"about_ca_topic_score_gemma":8.976821e-7,"domain_scores_codex":[0.9986074,0.00004723921,0.0003310062,0.0005204845,0.0001688873,0.0003249888],"domain_scores_gemma":[0.9989315,0.0001979391,0.00005599704,0.0005989175,0.0001198341,0.00009583827],"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":[0.00002847863,0.00005575894,0.000008535137,0.000009083667,0.00001182802,2.474236e-7,0.0001589693,0.7924119,2.118191e-7,0.0001258546,0.0000184069,0.2071707],"study_design_scores_gemma":[0.0008185729,0.0001889039,0.00005065553,0.00004933801,0.00001171458,0.000002890273,0.00000216477,0.9973732,0.000007653937,0.0009132245,0.0003218102,0.000259865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01589255,0.00006980406,0.9826216,0.0001587186,0.0001734516,0.0003355475,0.00000470844,0.0006953829,0.00004824581],"genre_scores_gemma":[0.695967,0.00007500836,0.3033229,0.0003789619,0.00008998616,0.00002660258,0.00001231527,0.00001484253,0.0001123807],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6800745,"threshold_uncertainty_score":0.9055052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02325071991968308,"score_gpt":0.2653314796262749,"score_spread":0.2420807597065918,"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."}}