{"id":"W4200091841","doi":"10.3390/telecom2040029","title":"A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks","year":2021,"lang":"en","type":"article","venue":"Telecom","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Computer science; Usability; Term (time); The Internet; Artificial intelligence; Data science; Wireless network; Wireless; Machine learning; Human–computer interaction; Telecommunications; World Wide Web","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":[0.001236609,0.00123731,0.000995404,0.003487979,0.0003713405,0.001691802,0.001481165,0.001000637,0.00214695],"category_scores_gemma":[0.00340038,0.0005766192,0.0008595212,0.006917262,0.0003697338,0.002773355,0.0005706167,0.001380854,0.001451239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005921659,"about_ca_system_score_gemma":0.000678441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00192387,"about_ca_topic_score_gemma":0.001528823,"domain_scores_codex":[0.9989985,0.0002197885,0.0001148789,0.0001620872,0.0004543647,0.00005041554],"domain_scores_gemma":[0.9976261,0.001534177,0.0001087948,0.0001547395,0.000540967,0.00003516608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005825049,0.0001560716,0.002661876,0.002582797,0.00012455,0.0001372413,0.00007299096,0.01497445,0.001616599,0.01410281,0.01733951,0.9461728],"study_design_scores_gemma":[0.00003754573,0.00048582,0.008811149,0.005469819,0.0003756302,0.001913651,0.0003164946,0.260472,0.009946221,0.06211713,0.6498202,0.0002343585],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01407813,0.5545175,0.385465,0.003054159,0.00158194,0.0003062486,0.000875197,0.001144481,0.0389774],"genre_scores_gemma":[0.08936597,0.7025971,0.1932663,0.001244186,0.002197757,0.0002956228,0.002217304,0.0001814395,0.008634294],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003487979,"threshold_uncertainty_score":0.0071823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05140448180371795,"score_gpt":0.2896884048320311,"score_spread":0.2382839230283131,"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."}}