{"id":"W4236719087","doi":"10.4108/icst.wiopt2008.2988","title":"A Markovian Model for Mobile Cellular Networks with QoS Differentiation","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Computer science; Quality of service; General Packet Radio Service; Cellular network; Computer network; Markov process; Distributed computing; Mobile QoS; Mobile telephony; Enhanced Data Rates for GSM Evolution; Mobile radio; Service (business); Telecommunications; Wireless; Mathematics","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":[0.0008352085,0.0009553746,0.000934022,0.0005281478,0.0009003665,0.001410833,0.001786461,0.001635498,0.003949318],"category_scores_gemma":[0.002771775,0.0005362238,0.0007816997,0.0009115674,0.001412277,0.002131132,0.0009441665,0.002218899,0.0008654151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002330566,"about_ca_system_score_gemma":0.001841788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01589385,"about_ca_topic_score_gemma":0.009874324,"domain_scores_codex":[0.9992633,0.0001773419,0.00002596318,0.0001263647,0.0002500449,0.0001569891],"domain_scores_gemma":[0.9989348,0.0005851718,0.000147424,0.00007257645,0.0001809408,0.00007918019],"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":[0.00002852733,0.00003045291,0.0002591392,0.00003105606,0.00001441647,0.0001718555,0.00007844302,0.6988754,0.001101965,0.2944802,0.001667267,0.003261198],"study_design_scores_gemma":[0.00001047134,0.000009684355,0.00005263975,0.000006516406,0.00000627533,0.0000382836,0.00001134383,0.9571865,0.0001414113,0.04111972,0.001407183,0.00001006244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01549568,0.00060285,0.9703904,0.001123243,0.0001869082,0.00006577106,0.0003803126,0.0001855481,0.01156919],"genre_scores_gemma":[0.8858067,0.002426206,0.07944631,0.0006608387,0.0004786898,0.000379842,0.0005619122,0.0001186943,0.03012072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01589385,"threshold_uncertainty_score":0.03160268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006863870767987023,"score_gpt":0.1711983658204824,"score_spread":0.1643344950524954,"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."}}