{"id":"W2145273166","doi":"10.1109/vetecf.2002.1040354","title":"Scheduling algorithms for the cdma2000 packet data evolution","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Scheduling (production processes); Network packet; Proportionally fair; Real-time computing; Round-robin scheduling; Computer network; Algorithm; Fair-share scheduling; Quality of service; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002489625,0.00008530645,0.00006557811,0.00001995707,0.0000957432,0.00002258412,0.0001820894,0.00004295028,0.00004695491],"category_scores_gemma":[0.00007911441,0.00006534457,0.00001654739,0.0001508554,0.00001457625,0.000241865,0.00001914919,0.00006688875,0.00001391337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005699176,"about_ca_system_score_gemma":0.0000136088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002580677,"about_ca_topic_score_gemma":0.00001333049,"domain_scores_codex":[0.999454,0.00001280215,0.0001164579,0.0001387158,0.00009403975,0.0001839494],"domain_scores_gemma":[0.9993451,0.0001145318,0.00001491299,0.0004599102,0.0000402349,0.00002531062],"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":[9.63935e-7,0.000003280539,0.00001814202,0.000005259461,0.00001486517,6.479569e-8,0.000009796824,0.9798206,0.00003859663,0.00507939,0.001399074,0.01360998],"study_design_scores_gemma":[0.0001975785,0.000003520528,0.00002259148,0.00000569666,0.00001807633,0.000001733312,0.00006330288,0.981511,0.0002797593,0.0008262368,0.01697703,0.00009348576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002481011,0.001195893,0.9950041,0.00003792227,0.0003830981,0.0002369495,0.00001491602,0.0002640369,0.002614982],"genre_scores_gemma":[0.5029907,0.0002786393,0.4958643,0.00003963561,0.0002435157,0.00005225687,0.00009647114,0.00005652168,0.0003779791],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5027426,"threshold_uncertainty_score":0.2664674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03883071349583778,"score_gpt":0.2666609228748086,"score_spread":0.2278302093789709,"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."}}