{"id":"W2963402703","doi":"","title":"QoS-Aware Power-Efficient Scheduler for LTE Uplink","year":2014,"lang":"en","type":"preprint","venue":"viXra","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Telecommunications link; Quality of service; Transmission (telecommunications); Computer network; Scheduling (production processes); Real-time computing; Power (physics); Distributed computing; Mathematical optimization; Telecommunications; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001358205,0.0003820272,0.0003968011,0.0001312147,0.00006808076,0.00006123159,0.0003035331,0.0004371775,0.00006483159],"category_scores_gemma":[0.00003470398,0.0004215694,0.0001643735,0.0001154113,0.00002817531,0.00003722782,0.0002097652,0.0004816493,0.00008771916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001662099,"about_ca_system_score_gemma":0.00002888585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001141091,"about_ca_topic_score_gemma":0.000001074207,"domain_scores_codex":[0.9985922,0.0000150933,0.0003573571,0.0004508838,0.0001648958,0.0004195705],"domain_scores_gemma":[0.9989233,0.00007867644,0.0001047166,0.0006632682,0.0001267337,0.000103314],"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":[0.000007813735,0.00001470233,0.00000875586,0.0002582461,0.00004294812,8.728207e-7,0.00006269605,0.9939747,0.0000786469,0.0004620441,0.002676799,0.00241173],"study_design_scores_gemma":[0.0002866304,0.00001777377,0.00004199896,0.0002074361,0.00002879691,9.670671e-7,0.00000894292,0.9852624,0.0004447768,0.0003667355,0.01287616,0.0004573954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008260299,0.0005398137,0.9842033,0.00006713882,0.00382831,0.0008096799,0.00005353652,0.0009062124,0.001331673],"genre_scores_gemma":[0.898136,0.0001461987,0.09907578,0.00008570909,0.001134984,0.0004440622,0.0004444499,0.0002807694,0.0002520112],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8898757,"threshold_uncertainty_score":0.9998236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009399865310517253,"score_gpt":0.2330266352303597,"score_spread":0.2236267699198424,"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."}}