{"id":"W3162910239","doi":"10.3390/telecom2020013","title":"A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks","year":2021,"lang":"en","type":"article","venue":"Telecom","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Handover; Computer science; Mobility management; Mobility model; Context (archaeology); Computer network; Cellular network; Vertical handover; Transmission (telecommunications); Real-time computing; Wireless network; Wireless; Telecommunications; Heterogeneous network","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":[0.0006182757,0.0006356497,0.0005601069,0.0006235253,0.000346362,0.0004612549,0.0009022132,0.0005701255,0.0006845311],"category_scores_gemma":[0.001111134,0.0002686178,0.0005036761,0.0005051197,0.0002352834,0.0008226329,0.0004957152,0.0006387852,0.0002053941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006657689,"about_ca_system_score_gemma":0.0006216404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327771,"about_ca_topic_score_gemma":0.0115683,"domain_scores_codex":[0.9996983,0.00007658499,0.0000170944,0.00007517249,0.00007222997,0.00006075411],"domain_scores_gemma":[0.9997141,0.00011769,0.00003585708,0.00002871816,0.00008200429,0.00002165163],"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":[0.00007691652,0.00009143467,0.002677909,0.00002476474,0.00005340626,0.00008776883,0.00005027734,0.8939262,0.003410905,0.002509356,0.000832723,0.09625844],"study_design_scores_gemma":[8.380579e-7,0.000009103529,0.0001613582,8.748339e-7,0.000003852788,0.000005294301,0.000003004099,0.9992825,0.0001720766,0.0003064942,0.00005261626,0.000001953778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08545028,0.0005125578,0.9116733,0.0002644233,0.0000555872,0.00003134731,0.00009106408,0.0007511792,0.001170199],"genre_scores_gemma":[0.9444274,0.0002846478,0.05354369,0.00007026699,0.0000430038,0.00003832797,0.0001933918,0.00002603486,0.001373334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01327771,"threshold_uncertainty_score":0.02640086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551460751236172,"score_gpt":0.2785902019624827,"score_spread":0.263075594450121,"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."}}