{"id":"W2539622116","doi":"10.1109/mascots.2014.10","title":"T-RATE: A Framework for the Trace-Driven Evaluation of 802.11 Rate Adaptation Algorithms","year":2014,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; TRACE (psycholinguistics); Throughput; Channel (broadcasting); Adaptation (eye); Process (computing); Variety (cybernetics); Wireless; Key (lock); Interference (communication); Distributed computing; Real-time computing; Algorithm; Computer network; Artificial intelligence; Telecommunications","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":[0.01263629,0.00258271,0.001603241,0.003160127,0.0009028999,0.003158786,0.005008846,0.001902331,0.003183152],"category_scores_gemma":[0.0405831,0.001008217,0.001621647,0.001574885,0.001264367,0.00311525,0.002754494,0.003560705,0.0008654313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001722093,"about_ca_system_score_gemma":0.00332983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01074351,"about_ca_topic_score_gemma":0.006136201,"domain_scores_codex":[0.9911107,0.003825327,0.0009837901,0.0006357875,0.003069563,0.0003748211],"domain_scores_gemma":[0.9824337,0.009289163,0.001431817,0.002663937,0.003652912,0.0005284601],"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":[0.0005986638,0.000695802,0.005410301,0.0007381529,0.0003750733,0.0003733731,0.0004903745,0.7996083,0.011217,0.06075665,0.01122906,0.1085072],"study_design_scores_gemma":[0.00005452151,0.0001739475,0.0002666016,0.00005223145,0.0000291416,0.00007591749,0.0000478315,0.9830032,0.003799663,0.008015806,0.004431805,0.00004931168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007019589,0.0001768725,0.9804863,0.0001627544,0.0001202814,0.0004982737,0.0006248623,0.008875614,0.002035432],"genre_scores_gemma":[0.2230307,0.0005470139,0.7689385,0.0001940356,0.0001083044,0.002015473,0.002093749,0.001797171,0.00127514],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01263629,"threshold_uncertainty_score":0.06682789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07356414109554829,"score_gpt":0.3341617212457472,"score_spread":0.260597580150199,"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."}}