{"id":"W2974803701","doi":"10.1109/access.2019.2942114","title":"Self-Organizing TDMA: A Distributed Contention-Resolution MAC Protocol","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Time division multiple access; Computer science; Computer network; Throughput; Channel (broadcasting); Node (physics); Transmission (telecommunications); Asynchronous communication; Frame (networking); Queue; Channel allocation schemes; Channel access method; Distributed computing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002698663,0.0001698411,0.0002002148,0.00006323196,0.0001508108,0.0007094971,0.001454934,0.0001015978,0.00007052123],"category_scores_gemma":[0.00001082526,0.0001505208,0.0000732619,0.0006112917,0.00001673342,0.001419409,0.0003017067,0.0001788328,0.0002638803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007867586,"about_ca_system_score_gemma":0.00008132425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004748464,"about_ca_topic_score_gemma":0.000009887577,"domain_scores_codex":[0.9984844,0.00009003526,0.0002949651,0.0004442979,0.0003020674,0.0003842235],"domain_scores_gemma":[0.9988742,0.00005956248,0.0001857722,0.0006168363,0.0001663,0.00009732033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009415463,0.004427887,0.336675,0.004913259,0.0009378633,0.0003817742,0.002705168,0.03017333,0.03096132,0.2438659,0.1922699,0.1517472],"study_design_scores_gemma":[0.00652061,0.0004592932,0.0327806,0.000508762,0.00002333473,0.00004909313,0.00001937722,0.6693303,0.022694,0.006465293,0.2597709,0.001378419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006554184,0.000009015148,0.7463824,0.0004525679,0.0007395925,0.2433848,0.000009688833,0.0007330253,0.001734758],"genre_scores_gemma":[0.6424496,0.000001270545,0.00543133,0.0003797458,0.0003738001,0.351035,0.00001246651,0.00002911426,0.0002876924],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.740951,"threshold_uncertainty_score":0.6841698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257466763989327,"score_gpt":0.3072242323356557,"score_spread":0.281477555936723,"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."}}