{"id":"W1971346204","doi":"10.1109/tvt.2011.2178622","title":"Efficient Scheduling Algorithms for Multiantenna CDMA Systems","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Scheduling (production processes); Code division multiple access; Electronic engineering; Cellular radio; Algorithm; Computer network; Engineering; Base station; Mathematical optimization; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001178434,0.0009748594,0.0008420192,0.0008594319,0.0007955029,0.001322415,0.001002416,0.000864215,0.003005309],"category_scores_gemma":[0.002777407,0.0004411539,0.0003781482,0.001523368,0.0007301773,0.001073104,0.0009719595,0.0007605782,0.000855301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001745317,"about_ca_system_score_gemma":0.002430141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004997483,"about_ca_topic_score_gemma":0.005752411,"domain_scores_codex":[0.9991699,0.0003471669,0.00003233519,0.0001106285,0.0002172399,0.0001228048],"domain_scores_gemma":[0.9990508,0.0005789596,0.0001071737,0.00008693057,0.0001364075,0.00003972016],"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":[0.00005866494,0.00003699602,0.0001318587,0.00005951654,0.000013704,0.00002017373,0.00005819287,0.9013777,0.001127359,0.03720229,0.001888163,0.05802533],"study_design_scores_gemma":[0.00002097447,0.00001134847,0.00002977786,0.000004465944,0.000002360111,0.000005811128,0.00001211716,0.9817914,0.0003119912,0.01703505,0.0007714325,0.000003427984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01132296,0.0005859344,0.9827958,0.0002653781,0.00006844197,0.00008859245,0.00007850474,0.0003983387,0.004396053],"genre_scores_gemma":[0.4369265,0.001093879,0.5555476,0.0002204325,0.0001263175,0.0004185378,0.0003092683,0.0001852282,0.005172251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004997483,"threshold_uncertainty_score":0.01266319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01926873368938945,"score_gpt":0.2212754563483852,"score_spread":0.2020067226589958,"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."}}