{"id":"W2101467233","doi":"10.1109/vetecf.2003.1285308","title":"Adaptive resource management for multimedia wireless networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Bandwidth (computing); Computer network; Dynamic bandwidth allocation; Call blocking; Bandwidth allocation; Wireless; Blocking (statistics); Scheme (mathematics); Bandwidth management; Wireless network; Adaptation (eye); Call Admission Control; Quality of service; Distributed computing; Multimedia; Telecommunications; Mathematics","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":[0.0006074593,0.0004745866,0.0003841377,0.0004203818,0.0005469749,0.0006660282,0.001174269,0.0005801177,0.001130667],"category_scores_gemma":[0.001192311,0.0001561846,0.0001994003,0.0005091924,0.0004689718,0.001009466,0.0007015918,0.0007837812,0.0002835558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005350755,"about_ca_system_score_gemma":0.0003224974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009540592,"about_ca_topic_score_gemma":0.001066109,"domain_scores_codex":[0.9996377,0.0001092974,0.000020893,0.00004751161,0.0001468543,0.00003781744],"domain_scores_gemma":[0.999635,0.000166624,0.00004466471,0.00004571131,0.00008014952,0.0000277444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002857512,0.0001521794,0.0009732821,0.0004794342,0.000127211,0.0006211784,0.0002693819,0.1701667,0.04548936,0.1362383,0.01174806,0.6334492],"study_design_scores_gemma":[0.00005583576,0.0001708082,0.0005966263,0.00006076395,0.00006143468,0.0004197005,0.0000705892,0.9055678,0.01000926,0.04947464,0.03346453,0.00004796544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01849875,0.01069054,0.9612419,0.0007093642,0.000409024,0.0001197436,0.00003663951,0.0006441018,0.007649919],"genre_scores_gemma":[0.7547015,0.006575125,0.2271477,0.0005540341,0.0008398788,0.0003180913,0.0001268192,0.00007667507,0.009660232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001174269,"threshold_uncertainty_score":0.003882229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03187402622503276,"score_gpt":0.2788481910562979,"score_spread":0.2469741648312651,"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."}}