{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001015374,0.00007857034,0.0001548295,0.00005637688,0.0002933236,0.00006882982,0.0001307675,0.00005870788,0.0004052361],"category_scores_gemma":[0.00005821589,0.00008519477,0.0001103822,0.0003398939,0.00008570447,0.0001080432,0.00002808201,0.00009886356,0.000006166807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001921078,"about_ca_system_score_gemma":0.00008712301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008158004,"about_ca_topic_score_gemma":0.004216674,"domain_scores_codex":[0.9987948,0.0001886471,0.0002289348,0.0003365273,0.0001893289,0.0002617963],"domain_scores_gemma":[0.9994354,0.0001155973,0.00004223092,0.0002522732,0.00009293978,0.00006154679],"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":[0.0002597103,0.005627622,0.08743609,0.0004267049,0.0005665402,0.00002668067,0.0105884,0.5381356,0.000038662,0.02650106,0.01874956,0.3116434],"study_design_scores_gemma":[0.003278733,0.0001383192,0.04347734,0.00005025284,0.0003074167,0.000001250724,0.01388043,0.5378097,0.0003287058,0.007308896,0.3927153,0.0007036276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4401741,0.0007386343,0.531943,0.000286794,0.0002779297,0.002237404,0.00003927285,0.0001828222,0.02412005],"genre_scores_gemma":[0.9943743,0.0001136551,0.001335852,0.0001507452,0.0001687395,0.0006625902,0.0001464082,0.000007392501,0.003040284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5542002,"threshold_uncertainty_score":0.4437051,"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."}}