{"id":"W2113899967","doi":"10.1109/wcnc.2007.590","title":"A Vertical Handoff Decision Algorithm for Heterogeneous Wireless Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"IPv6, Mobility, Handover, Networks, Security","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Handover; Computer science; Vertical handover; Wireless network; Weighting; Computer network; Markov decision process; Wireless; Markov chain; Markov process; Terminal (telecommunication); Heterogeneous network; Algorithm; Distributed computing; Mathematics; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006131071,0.0002843977,0.0003442451,0.00007124645,0.0001034317,0.00006819658,0.0002146969,0.0003082199,0.00008927507],"category_scores_gemma":[0.00002882486,0.0002701996,0.0001897607,0.0002076518,0.00005753676,0.0001127362,0.00007468507,0.0002582662,0.00002862319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001588011,"about_ca_system_score_gemma":0.00001104275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001416428,"about_ca_topic_score_gemma":0.0002777787,"domain_scores_codex":[0.9980526,0.00001747591,0.0004690483,0.000358883,0.0002589542,0.0008430143],"domain_scores_gemma":[0.9986303,0.0005959948,0.00001651561,0.0004179597,0.00008500758,0.0002542628],"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":[0.0002287022,0.0001417878,0.0002253149,0.00004314267,0.0001029215,0.00005038624,0.00008865623,0.0817932,0.0002220079,0.0003600196,0.003126418,0.9136174],"study_design_scores_gemma":[0.001390438,0.00009306268,0.0002759311,0.00002702193,0.00003426011,0.00003395185,0.00001164947,0.9875226,0.005074027,0.0006049948,0.004593114,0.000338956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1443495,0.0008299316,0.8515375,0.000006759372,0.001674231,0.0005107112,0.000006188029,0.00050297,0.0005822092],"genre_scores_gemma":[0.977308,0.00007114442,0.02141757,0.0001409521,0.0008466945,0.00004825576,0.00002015002,0.00008163338,0.00006561597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9132785,"threshold_uncertainty_score":0.999975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007250445412456505,"score_gpt":0.2313313307687566,"score_spread":0.2240808853563001,"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."}}