{"id":"W2125879620","doi":"10.1109/qshine.2005.37","title":"On Maintaining Multimedia Session's Quality in CDMA Cellular Networks Using a Rate Adaptive Framework","year":2005,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Admission control; Computer network; Call Admission Control; Base station; Bandwidth (computing); Cellular network; Session (web analytics); Bandwidth allocation; Throughput; Quality of service; Wireless network; A priori and a posteriori; Code division multiple access; Wireless; Multimedia; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003405487,0.001271437,0.001240809,0.0008945164,0.0008935427,0.001754141,0.001805784,0.001088251,0.0008811488],"category_scores_gemma":[0.007187704,0.0005017029,0.0005645044,0.001185529,0.001757723,0.002456478,0.001182242,0.001504955,0.0002111827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002208718,"about_ca_system_score_gemma":0.001061334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007448609,"about_ca_topic_score_gemma":0.006334676,"domain_scores_codex":[0.9980938,0.0007040789,0.00006941777,0.0002274281,0.0007062844,0.0001991104],"domain_scores_gemma":[0.9970093,0.001976791,0.0001935242,0.0002240192,0.0004942079,0.0001021519],"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":[0.000234337,0.0001344054,0.0009666978,0.0001743419,0.00007103563,0.0001543071,0.0003050379,0.7742066,0.01002128,0.1023842,0.00142365,0.109924],"study_design_scores_gemma":[0.000009297577,0.00003975993,0.0001115108,0.000007601049,0.00001461859,0.00003140235,0.00001249178,0.9917427,0.0007960307,0.006823372,0.0003976178,0.0000135666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02023432,0.002109149,0.9749153,0.0002638282,0.00005397966,0.00007490104,0.00002278935,0.0001442108,0.00218156],"genre_scores_gemma":[0.8026989,0.003314651,0.1910266,0.0001471246,0.0002731351,0.0001581516,0.00004823894,0.00007842254,0.002254857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007448609,"threshold_uncertainty_score":0.01801014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06743828972064224,"score_gpt":0.3572991631849067,"score_spread":0.2898608734642645,"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."}}