{"id":"W2147626391","doi":"10.1109/icc.1996.541315","title":"On prediction of bursty traffic in broadband satcom systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Telecommunications link; Computer science; Queue; Broadband networks; Artificial neural network; Traffic generation model; Real-time computing; Adaptive neuro fuzzy inference system; Computer network; Broadband; Fuzzy control system; Fuzzy logic; Artificial intelligence; Telecommunications","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":[0.0003116493,0.00005976185,0.000113063,0.0001634463,0.00003420773,0.00005173298,0.000815569,0.00005015222,0.00003928605],"category_scores_gemma":[0.0000238363,0.00005155773,0.00002367947,0.000533302,0.00003313315,0.0001831648,0.0001145717,0.0001573668,0.00004061303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005126961,"about_ca_system_score_gemma":0.00001029136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006120847,"about_ca_topic_score_gemma":0.00002577338,"domain_scores_codex":[0.9990028,0.0001650589,0.0002368044,0.0001699249,0.0002667091,0.0001586945],"domain_scores_gemma":[0.9988447,0.0002227643,0.0000468188,0.0007923948,0.00004902003,0.00004426881],"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":[0.00001780524,0.001028166,0.004686748,0.00008242347,0.00002775729,0.00000889345,0.002072732,0.5360913,0.0003735,0.2568715,0.02863912,0.1701001],"study_design_scores_gemma":[0.0002737053,0.00007305149,0.005193987,0.0000435355,3.541744e-7,0.000002432167,0.00001616614,0.9935999,0.00006006558,0.00006759269,0.0006277949,0.0000414488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8188147,0.001447379,0.1259432,0.001873879,0.0004704228,0.0007233617,0.000004089493,0.0003546165,0.05036838],"genre_scores_gemma":[0.9982619,0.0001278764,0.0006938989,0.00001997421,0.00001543271,0.00001683795,9.42793e-7,0.000003940535,0.0008592089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4575085,"threshold_uncertainty_score":0.2102463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04502849668792305,"score_gpt":0.2550722290464295,"score_spread":0.2100437323585065,"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."}}