{"id":"W2066218456","doi":"10.1109/iswpc.2008.4556217","title":"Fair scheduling in multirate wireless access networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer network; Maximum throughput scheduling; Scheduling (production processes); Link adaptation; Wireless network; Physical layer; Wireless distribution system; Proportionally fair; Media access control; Wireless; Access control; Provisioning; Link layer; Access network; Round-robin scheduling; Distributed computing; Quality of service; Dynamic priority scheduling; Channel (broadcasting); Wi-Fi; Fading; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004978697,0.0001541091,0.0001701081,0.00009837117,0.00004279693,0.00002250834,0.0001805338,0.0001018047,0.00003390653],"category_scores_gemma":[0.000006567042,0.0001638223,0.00002667097,0.0004978142,0.0000278251,0.0005041319,0.00004535151,0.0002147418,0.0000154246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000711189,"about_ca_system_score_gemma":0.000007893706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001531144,"about_ca_topic_score_gemma":0.00007647065,"domain_scores_codex":[0.9991597,0.00001393113,0.0002397044,0.0001736493,0.00008982937,0.0003231762],"domain_scores_gemma":[0.999682,0.00004258701,0.00002329108,0.0001674388,0.00002626531,0.00005843188],"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":[0.000004189059,0.000009372434,0.007227761,0.000008371917,0.000005220846,0.00001838522,0.00004827933,0.9876327,0.00008260093,0.0001168847,0.0001250738,0.00472115],"study_design_scores_gemma":[0.0003339275,0.000003604465,0.002716197,0.00002808741,0.000001357068,0.000005655339,0.00001414566,0.9960635,0.0005175002,0.00002459384,0.00008296184,0.0002084751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3241903,0.000176712,0.6720341,0.000009861558,0.0002002384,0.0001118337,3.136391e-7,0.0004648848,0.002811824],"genre_scores_gemma":[0.9824936,0.0008892615,0.0162329,0.00004969897,0.0001200357,0.000025291,0.00001200559,0.00005249753,0.000124673],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6583034,"threshold_uncertainty_score":0.6680481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01778363761885003,"score_gpt":0.2336172245206478,"score_spread":0.2158335869017977,"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."}}