{"id":"W3016390056","doi":"10.1109/lcomm.2020.2987639","title":"Completion-Time-Driven Scheduling for Uplink NOMA-Enabled Wireless Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council","keywords":"Noma; Telecommunications link; Computer science; Scheduling (production processes); Wireless network; Wireless; Computer network; Distributed computing; Pairing; Computational complexity theory; Mathematical optimization; Algorithm; Mathematics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001130815,0.0002529367,0.0003414001,0.000109195,0.000430939,0.00007158846,0.002602017,0.0001388974,0.00001254902],"category_scores_gemma":[0.00007369158,0.0003053379,0.0001248135,0.0004901857,0.0002696606,0.0002490882,0.0003264763,0.0005836186,0.00007962502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001185374,"about_ca_system_score_gemma":0.00001413802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003416701,"about_ca_topic_score_gemma":0.000004112148,"domain_scores_codex":[0.9987575,0.00006424152,0.0004650324,0.0002432514,0.0001090059,0.0003609802],"domain_scores_gemma":[0.9966598,0.0005760744,0.0001192186,0.002440477,0.0001047109,0.00009975967],"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":[0.000008249954,0.00002321262,0.0001259604,0.0000397387,0.00007725418,4.119109e-7,0.0001848156,0.9031466,0.0841954,0.001493787,0.004231552,0.006473017],"study_design_scores_gemma":[0.0004338508,0.00001659678,0.00006667179,0.00004462016,0.0000197667,0.000001682867,0.00007637214,0.9810081,0.00325096,0.00008867999,0.01465907,0.0003335912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02454247,0.0009151237,0.9484406,0.02262494,0.0001161279,0.0006149978,0.00001769715,0.002485345,0.0002427216],"genre_scores_gemma":[0.7955715,0.0009021559,0.201187,0.001638088,0.00007313472,0.0003963142,0.0001407811,0.00008430587,0.000006687813],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.771029,"threshold_uncertainty_score":0.9999399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03607211356565994,"score_gpt":0.2503668193652092,"score_spread":0.2142947057995492,"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."}}