{"id":"W2206917860","doi":"10.5296/npa.v7i3.8229","title":"Channel Aware Scheduling Algorithm for LTE Uplink and Downlink","year":2015,"lang":"en","type":"article","venue":"Network Protocols and Algorithms","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Telecommunications link; Algorithm; Scheduling (production processes); Computer network; Distributed computing; Mathematical optimization","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":[0.0003108276,0.0004548591,0.0004284098,0.0004204103,0.0006691231,0.0007017784,0.0006108388,0.0003864451,0.002332336],"category_scores_gemma":[0.0008408365,0.0001332312,0.0002925257,0.0004791099,0.0002283808,0.0004003387,0.0004595292,0.0004767226,0.0006422508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008235474,"about_ca_system_score_gemma":0.00174721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00601085,"about_ca_topic_score_gemma":0.006498368,"domain_scores_codex":[0.9996428,0.00005956423,0.00002299123,0.00007823436,0.0001318987,0.00006438059],"domain_scores_gemma":[0.9997779,0.00005470749,0.00003031937,0.00002376284,0.00009876993,0.0000146658],"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":[0.0002616953,0.0001119159,0.001974205,0.0001694055,0.00005509803,0.0002115732,0.0001795229,0.5121118,0.01790687,0.03784961,0.01544645,0.4137218],"study_design_scores_gemma":[0.00002641761,0.00005715686,0.000300614,0.000009058771,0.000009137781,0.0001168305,0.00002972303,0.9845578,0.002639182,0.005166763,0.007074002,0.00001319141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01339086,0.0007402322,0.9740045,0.0002601038,0.0002401579,0.000158299,0.0001705279,0.0008996902,0.01013558],"genre_scores_gemma":[0.6277292,0.0007228443,0.3599437,0.0002926636,0.0001808585,0.0003661213,0.0005470852,0.00008269872,0.01013473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00601085,"threshold_uncertainty_score":0.01195174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03079740783755786,"score_gpt":0.279223523262259,"score_spread":0.2484261154247011,"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."}}