{"id":"W2137821592","doi":"10.1109/hoti.2012.16","title":"Caliper: Precise and Responsive Traffic Generator","year":2012,"lang":"en","type":"article","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Calipers; Generator (circuit theory); Computer science; Computer graphics (images); Engineering; Physics; Mechanical engineering; Power (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002216621,0.00008486969,0.00009265672,0.00003674267,0.00008009293,0.00007289686,0.0001939171,0.00004237502,0.00003998121],"category_scores_gemma":[0.00001934116,0.00006747585,0.00002450631,0.0001242333,0.0000267889,0.0003490699,0.00006165548,0.0000603019,0.00007691244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001031605,"about_ca_system_score_gemma":0.00002968521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001571264,"about_ca_topic_score_gemma":0.000003825997,"domain_scores_codex":[0.9992923,0.00006353232,0.0001035338,0.0001742254,0.0001133563,0.0002530404],"domain_scores_gemma":[0.999422,0.00009436193,0.0000219648,0.0002329092,0.00003718074,0.000191653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001366394,0.00004496117,0.0003827775,0.000001813754,0.00001216529,0.000003152869,0.0007816202,0.0001832515,0.0001502774,0.06392728,0.01290903,0.92159],"study_design_scores_gemma":[0.001823665,0.0001948439,0.008148068,0.00001741786,0.0000303684,0.0001513024,0.0001807683,0.6778568,0.001061027,0.0002595832,0.3095172,0.0007589464],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7715725,0.002971205,0.2190044,0.002346021,0.0008141871,0.0002062427,0.000001132497,0.0003942057,0.002690083],"genre_scores_gemma":[0.9860037,0.00003262905,0.01086976,0.0008273972,0.0002592815,0.00001513492,3.550481e-7,0.00000442602,0.001987298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9208311,"threshold_uncertainty_score":0.2751585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01016318296056663,"score_gpt":0.2157338263721544,"score_spread":0.2055706434115878,"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."}}