{"id":"W2585861133","doi":"10.1109/glocom.2016.7841857","title":"Gaussian Process Regression Based Traffic Modeling and Prediction in High-Speed Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kriging; Computer science; Gaussian process; Hurst exponent; Traffic generation model; Ground-penetrating radar; Range (aeronautics); Data mining; Covariance; Artificial intelligence; Machine learning; Data modeling; Covariance function; Gaussian; Algorithm; Real-time computing; Covariance matrix; Radar; Engineering; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001999091,0.0001532624,0.0001526222,0.000141846,0.00008508071,0.0001249366,0.0003549077,0.000147748,0.00002921216],"category_scores_gemma":[0.00002061207,0.00008728087,0.00002000254,0.0003872849,0.0000319396,0.0007794871,0.00006262521,0.0001400769,0.000005950729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002994559,"about_ca_system_score_gemma":0.00007783792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001198864,"about_ca_topic_score_gemma":0.00002327851,"domain_scores_codex":[0.9987391,0.00003417923,0.0002563314,0.0004749866,0.0001944981,0.0003009559],"domain_scores_gemma":[0.9994543,0.00003994727,0.00006348079,0.0002744126,0.00005512796,0.0001126971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001341294,0.0003328405,0.01448396,0.000242851,0.00001536676,0.00006119314,0.0007717616,0.2748447,0.001224498,0.04569105,0.0004769104,0.6617207],"study_design_scores_gemma":[0.0006827051,0.0000635312,0.002440619,0.0003318645,0.000002103478,0.000006413498,0.00001418446,0.9937733,0.0002092296,0.002321959,0.000009629875,0.0001444598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1481168,0.00005898443,0.8488172,0.002186961,0.0001087401,0.0001138135,9.036683e-7,0.0001757659,0.0004207757],"genre_scores_gemma":[0.9915317,0.00002823541,0.008107128,0.0001467554,0.00004537789,0.00001048095,0.000001210987,0.000008392473,0.0001207156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8434148,"threshold_uncertainty_score":0.355921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01162050739724903,"score_gpt":0.2290844282481626,"score_spread":0.2174639208509136,"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."}}