{"id":"W1544015136","doi":"10.1002/0471643505.ch5","title":"Markov Chains: Application to Multiplexing and Access","year":2004,"lang":"en","type":"other","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Aloha; Markov chain; Asynchronous communication; Multiplexing; Statistical time division multiplexing; Computer science; Random access; Simple (philosophy); Time-division multiplexing; Poisson distribution; Markov process; Throughput; Algorithm; Computer network; Mathematics; Telecommunications; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001821772,0.000170935,0.0001442353,0.000149902,0.00001530025,0.00003089812,0.0001099268,0.000147297,0.0001404057],"category_scores_gemma":[0.000004148148,0.0001841037,0.00001220218,0.000158734,0.000009005624,0.00005992518,0.00004115474,0.0000755969,0.00002628562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006589952,"about_ca_system_score_gemma":0.000004342284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003444994,"about_ca_topic_score_gemma":0.0002007223,"domain_scores_codex":[0.9994869,0.000003063196,0.000101465,0.0001980772,0.00006690408,0.0001435422],"domain_scores_gemma":[0.9997115,0.000008654007,0.00002653244,0.0001843722,0.00000803784,0.00006093002],"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":[0.000001145316,0.000003063643,0.00002079462,0.0001077033,0.00001497431,4.876044e-7,0.0000180706,0.9164983,0.00004270168,0.0004510675,0.03597717,0.04686452],"study_design_scores_gemma":[0.0003650324,0.000007485055,0.00009357341,0.0003399273,0.00001138473,0.000001958545,0.000009443258,0.5585802,0.0001615526,0.0001076207,0.4396893,0.00063249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.000002535607,0.000458512,0.7588582,0.00001640672,0.00009038143,0.0004168317,0.000006901211,0.0009117056,0.2392385],"genre_scores_gemma":[0.02037911,0.003182543,0.453316,0.0004601398,0.001616702,0.0007904842,0.0005725246,0.003165266,0.5165172],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4037122,"threshold_uncertainty_score":0.750753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005429707492074638,"score_gpt":0.2343889149373376,"score_spread":0.2289592074452629,"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."}}