{"id":"W2146989178","doi":"10.1109/glocom.2006.925","title":"WSN01-1: Frame Aggregation and Optimal Frame Size Adaptation for IEEE 802.11n WLANs","year":2006,"lang":"en","type":"article","venue":"Globecom","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":221,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Frame (networking); Computer network; Protocol data unit; Throughput; Network packet; Channel (broadcasting); Network allocation vector; Local area network; Physical layer; Protocol (science); IEEE 802.11; Multiple Access with Collision Avoidance for Wireless; Wireless; 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":[0.001546138,0.0007178536,0.0006295224,0.0005472255,0.0003073856,0.0004567239,0.0009181796,0.0004731477,0.0006993845],"category_scores_gemma":[0.002201164,0.0002816069,0.0003733455,0.0007174908,0.0002990174,0.0007789225,0.0004503179,0.0004578346,0.0002598235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007272252,"about_ca_system_score_gemma":0.000693781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003673945,"about_ca_topic_score_gemma":0.004453284,"domain_scores_codex":[0.9995735,0.0001290698,0.00002987966,0.00008292082,0.0001366946,0.00004792467],"domain_scores_gemma":[0.999629,0.0001375238,0.00006008522,0.00004600189,0.0001071664,0.00002027131],"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":[0.0003933227,0.0002794483,0.00271216,0.0002024957,0.00008553879,0.0001832408,0.0001857689,0.512603,0.03407522,0.01645151,0.005861291,0.426967],"study_design_scores_gemma":[0.00001625141,0.0001324363,0.0005285307,0.000009194341,0.00001303782,0.00007676546,0.0000127408,0.9924333,0.003791554,0.0008785375,0.002096239,0.00001142792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04245751,0.001091302,0.9524127,0.0001503296,0.000113241,0.0001220689,0.00005621254,0.0009579089,0.002638816],"genre_scores_gemma":[0.6106223,0.001411147,0.3839287,0.00007416443,0.0001403113,0.0001918487,0.0002943718,0.0001291884,0.003207835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003673945,"threshold_uncertainty_score":0.008176863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01261691852500165,"score_gpt":0.2411336831908688,"score_spread":0.2285167646658671,"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."}}