{"id":"W2105331599","doi":"10.1109/sbac-pad.2006.11","title":"Characterizing the Performance of Data Management Systems on Hyper-Threaded Architectures","year":2006,"lang":"en","type":"article","venue":"Proceedings","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Pentium; Multithreading; Benchmark (surveying); Operating system; Computer architecture; Performance improvement; Simultaneous multithreading; Memory management; Embedded system; Parallel computing","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":[],"consensus_categories":[],"category_scores_codex":[0.0003484664,0.00009910468,0.0001083482,0.00008915238,0.0001243508,0.0001419589,0.001582895,0.00002436963,5.206949e-7],"category_scores_gemma":[0.000005865443,0.00006721448,0.00001778029,0.0002452934,0.00003247332,0.000196038,0.000445304,0.00009047832,0.000004652828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001224989,"about_ca_system_score_gemma":0.000006433604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001610284,"about_ca_topic_score_gemma":9.634255e-8,"domain_scores_codex":[0.9991079,0.000007391212,0.0001992931,0.0002844692,0.0002383929,0.0001625733],"domain_scores_gemma":[0.9993802,0.00002264238,0.0001465003,0.0003773079,0.00005664789,0.00001669165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000134742,0.0007137918,0.01914246,0.002337205,0.0001826141,0.000008810301,0.003852092,0.02591766,0.01858546,0.6523274,0.03618868,0.2406091],"study_design_scores_gemma":[0.0002092679,0.0001342649,0.00931024,0.0002862755,0.00001130019,0.0000235581,0.00003658921,0.958294,0.01825459,0.0005225366,0.01267645,0.0002409405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8127603,0.00029247,0.1362236,0.001546026,0.0002940248,0.000750849,0.000005384251,0.001000991,0.04712633],"genre_scores_gemma":[0.9699813,0.00002421162,0.02955843,0.0001184687,0.00007865259,0.0000148545,0.000004065379,0.000006631432,0.0002134618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9323763,"threshold_uncertainty_score":0.2941439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02780707180505273,"score_gpt":0.2490424266302177,"score_spread":0.2212353548251649,"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."}}