{"id":"W2291613467","doi":"10.1109/coconet.2015.7411177","title":"Economic access network selection in heterogeneous wireless networks environment","year":2015,"lang":"en","type":"article","venue":"","topic":"IPv6, Mobility, Handover, Networks, Security","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; École de Technologie Supérieure","funders":"","keywords":"Computer science; Wireless network; Heterogeneous network; Computer network; Heterogeneous wireless network; Radio resource management; Access network discovery and selection function; Access network; Wireless; Selection (genetic algorithm); Radio access technology; Throughput; Radio access network; Revenue; Network Access Device; Telecommunications; Base station; Machine learning; User equipment","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":[0.00152362,0.0003292933,0.0005238121,0.0004959815,0.0004031542,0.0007689089,0.0006667951,0.0003646963,0.000842972],"category_scores_gemma":[0.002378168,0.0002000612,0.000316681,0.0005296011,0.000575016,0.001061184,0.0006425398,0.0003323025,0.00009429864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008935904,"about_ca_system_score_gemma":0.0006849203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002430936,"about_ca_topic_score_gemma":0.002809818,"domain_scores_codex":[0.9988655,0.0005245552,0.0000306827,0.0001014954,0.0002992391,0.0001785487],"domain_scores_gemma":[0.9986708,0.0008850706,0.0001324603,0.00007674004,0.0001324635,0.0001024438],"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":[0.0002031863,0.00009371171,0.004601564,0.00003556963,0.00006343979,0.0004573881,0.00005736349,0.92241,0.005246079,0.02407689,0.0008272108,0.0419275],"study_design_scores_gemma":[0.000008221858,0.00002618494,0.000543623,0.000001458981,0.000009176968,0.00005446643,0.00001520458,0.993942,0.0006313009,0.004523708,0.0002400185,0.000004623632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.233272,0.00033112,0.7623814,0.0002507483,0.00003672094,0.00007599023,0.00005700515,0.000145019,0.003450067],"genre_scores_gemma":[0.9859937,0.0001039974,0.01316789,0.00001891799,0.0000112988,0.00001526396,0.00002488132,0.000008086152,0.000655976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002430936,"threshold_uncertainty_score":0.008057714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331654588384016,"score_gpt":0.2149271449001406,"score_spread":0.2016105990163005,"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."}}