{"id":"W2111035283","doi":"10.1109/vetecf.2008.400","title":"Backoff Strategies in Hiperlan\\2 with Error Control Protocol","year":2008,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"HiperLAN; Computer science; Exponential backoff; Markov chain; Throughput; Random access; Channel (broadcasting); Frame (networking); Distributed coordination function; Markov model; Error detection and correction; Computer network; Algorithm; Telecommunications; IEEE 802.11; Wireless; Wireless lan","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":[0.002263145,0.0008165319,0.000891069,0.0006515861,0.0005963299,0.0009902857,0.001248706,0.0007849685,0.0006940194],"category_scores_gemma":[0.005344685,0.000288102,0.0003390451,0.0004210094,0.0007231905,0.001379728,0.0005783359,0.0006296965,0.000106069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008942283,"about_ca_system_score_gemma":0.001020823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002308998,"about_ca_topic_score_gemma":0.001806425,"domain_scores_codex":[0.9982188,0.0005720211,0.00007442138,0.0001736366,0.0006020815,0.0003589783],"domain_scores_gemma":[0.9964185,0.002340556,0.0004691691,0.0001809379,0.0004955167,0.00009528494],"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":[0.0009239145,0.0005106108,0.003861279,0.0003435862,0.0001926291,0.001158525,0.0003896429,0.8304486,0.02248365,0.0407451,0.001464027,0.09747842],"study_design_scores_gemma":[0.00004240946,0.0003954152,0.0004189374,0.00001351533,0.00004701212,0.0002332683,0.00005150588,0.9902791,0.004431981,0.003503203,0.000559151,0.00002455038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4270414,0.004310446,0.561231,0.0004093091,0.0001999768,0.0002920931,0.00005585888,0.0004493794,0.006010584],"genre_scores_gemma":[0.972572,0.0005185509,0.0253395,0.00007668163,0.00003464348,0.00006605189,0.00001933765,0.0000215984,0.001351476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002308998,"threshold_uncertainty_score":0.01196879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02266024054735717,"score_gpt":0.2733036888167914,"score_spread":0.2506434482694342,"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."}}