{"id":"W2158231564","doi":"10.1145/2641798.2641819","title":"Handoff rate analysis in heterogeneous cellular networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Bell Canada Enterprises","keywords":"Computer science; Handover; Correctness; Randomness; Network topology; Overhead (engineering); Computer network; Mobility model; Cellular network; Distributed computing; Heterogeneous network; Trajectory; Wireless network; Algorithm; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001936195,0.0009883412,0.000597624,0.001886739,0.0004428819,0.0008278934,0.00115829,0.0006455203,0.0004391175],"category_scores_gemma":[0.006813137,0.0002951025,0.0005672676,0.001023633,0.000891125,0.001225153,0.0009609566,0.0005879362,0.0001507573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001227241,"about_ca_system_score_gemma":0.0004608718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003492175,"about_ca_topic_score_gemma":0.001141729,"domain_scores_codex":[0.9987617,0.0002783921,0.00006212105,0.0001856836,0.0005269097,0.000185128],"domain_scores_gemma":[0.9970162,0.001621026,0.000458591,0.0003251615,0.000476736,0.0001023888],"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":[0.00004800725,0.00002553452,0.00317388,0.00004300692,0.00003507081,0.0002011792,0.0000745298,0.9480533,0.005990141,0.02805462,0.0003860378,0.01391464],"study_design_scores_gemma":[0.000001268205,0.00001277567,0.0008076033,0.000003450753,0.000008343582,0.00004862915,0.00001927794,0.9946769,0.0006064091,0.00363247,0.0001748288,0.000008109257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1962136,0.001642045,0.7965808,0.0002197891,0.00004920061,0.00007910633,0.0001775834,0.0003480712,0.004689913],"genre_scores_gemma":[0.9871128,0.0006016088,0.01134655,0.00002950975,0.00004350121,0.00003305464,0.0000944183,0.00002555773,0.0007129277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003492175,"threshold_uncertainty_score":0.01023972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01433050859064766,"score_gpt":0.2516535831025933,"score_spread":0.2373230745119456,"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."}}