{"id":"W4242178347","doi":"10.1002/wcm.648","title":"An analytical model for reverse data channel scheduling techniques in cdma2000 1xEV‐DO","year":2008,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"CDMA2000; Computer science; Scheduling (production processes); Computer network; Markov chain; Markov model; Markov process; Wireless; Network packet; Real-time computing; Code division multiple access; Telecommunications; Mathematical optimization","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":[0.0003013383,0.0001654531,0.0002453466,0.0001385844,0.0003303459,0.00003560535,0.0008227666,0.0001038449,8.88575e-7],"category_scores_gemma":[0.00001666759,0.0001958401,0.00002421197,0.0002879029,0.0001219029,0.0003855254,0.0003876694,0.0002567261,5.394865e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005874563,"about_ca_system_score_gemma":0.00002909249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001452119,"about_ca_topic_score_gemma":0.00003036767,"domain_scores_codex":[0.9989436,0.00004386986,0.0003736332,0.0002933658,0.00007727598,0.0002682864],"domain_scores_gemma":[0.9979404,0.000167948,0.00006064398,0.001680566,0.00007289078,0.000077616],"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":[0.000003972193,0.00005890201,0.0001944174,0.00002719593,0.00000840763,6.12255e-7,0.0006316486,0.944286,0.0001373348,0.0005881022,0.00004057593,0.05402279],"study_design_scores_gemma":[0.0002219464,0.00002043828,0.00003168515,0.0001126586,0.000008980601,0.000008403656,0.0001839961,0.9985709,0.00005176217,0.0001096709,0.0004641894,0.0002153629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1040995,0.001153261,0.8936687,0.00003181041,0.00002688836,0.000475941,0.00002906638,0.0003608109,0.0001540141],"genre_scores_gemma":[0.7416524,0.003003007,0.2549306,0.00002213982,0.0000393034,0.00007464601,0.0002318491,0.00003931472,0.000006744949],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6387381,"threshold_uncertainty_score":0.7986128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0629657462963575,"score_gpt":0.3204124867766084,"score_spread":0.2574467404802509,"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."}}