{"id":"W2133541616","doi":"10.1109/icc.2007.272","title":"VoIP Capacity Allocation Using an Adaptive Voice Packetization Server in IEEE 802.11 WLANs","year":2007,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Voice over IP; Computer science; Computer network; Latency (audio); IEEE 802; Margin (machine learning); The Internet; Telecommunications; Quality of service; Operating system","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":[0.001684117,0.0004037478,0.0005063704,0.0005213975,0.0004069271,0.0007246583,0.001137888,0.0006294166,0.0004739257],"category_scores_gemma":[0.002903866,0.0003180478,0.0001943512,0.0004436634,0.0008150701,0.001240921,0.0006049307,0.0006521465,0.0002118044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006711509,"about_ca_system_score_gemma":0.0006140092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001696123,"about_ca_topic_score_gemma":0.001066975,"domain_scores_codex":[0.999292,0.0002588549,0.00004890433,0.0001025567,0.0001722313,0.0001252783],"domain_scores_gemma":[0.9985159,0.000737346,0.0001548255,0.0002204791,0.0002710636,0.0001004161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001103524,0.0004772446,0.006769843,0.0001161548,0.00009472555,0.0002640027,0.0002822524,0.644147,0.1311925,0.01581921,0.001037376,0.1986962],"study_design_scores_gemma":[0.00004438789,0.0001873881,0.001035665,0.000005624348,0.00002712544,0.0001375278,0.00002942684,0.9619732,0.03423473,0.001679852,0.0006202361,0.00002486771],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5157675,0.0004835255,0.4797169,0.0001429386,0.00004318959,0.00008565308,0.00001839643,0.0012852,0.002456826],"genre_scores_gemma":[0.9753293,0.00005452344,0.02403285,0.00002126883,0.00001468407,0.00002241758,0.00001564407,0.00001669029,0.0004927448],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001696123,"threshold_uncertainty_score":0.008906543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0743059115482863,"score_gpt":0.2975318256954965,"score_spread":0.2232259141472102,"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."}}