{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001922841,0.001328337,0.001064675,0.001265488,0.0007929655,0.001841301,0.0008607595,0.001043204,0.006394135],"category_scores_gemma":[0.008290991,0.0006722228,0.001019621,0.001976226,0.001850945,0.002107101,0.001219604,0.002267684,0.0008237122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00196476,"about_ca_system_score_gemma":0.001562373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004722989,"about_ca_topic_score_gemma":0.002895861,"domain_scores_codex":[0.9990902,0.0004103643,0.00004710689,0.0001088271,0.0002553503,0.00008805342],"domain_scores_gemma":[0.9946849,0.004317321,0.0002664037,0.0002147492,0.0004035236,0.0001130796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001613155,0.0000204149,0.0004026202,0.0000636141,0.00002764334,0.0001197546,0.0001266781,0.1477934,0.0004232534,0.8332623,0.001642372,0.01610173],"study_design_scores_gemma":[0.000007519177,0.00001807402,0.0001008332,0.00003570567,0.00001329226,0.00006754114,0.00002833765,0.6017705,0.0003183749,0.3923686,0.005258118,0.00001322491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003750188,0.001802936,0.9793084,0.0005739779,0.0001779616,0.00005381777,0.0000736114,0.0001485176,0.01411062],"genre_scores_gemma":[0.5389861,0.02169819,0.4021151,0.0007144515,0.001682997,0.0006908595,0.0003372848,0.0002992635,0.0334756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006394135,"threshold_uncertainty_score":0.02139056,"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."}}