{"id":"W3092706418","doi":"10.3991/ijim.v14i17.16639","title":"MROM Scheme to Improve Handoff Performance in Mobile Networks","year":2020,"lang":"en","type":"article","venue":"International Journal of Interactive Mobile Technologies (iJIM)","topic":"IPv6, Mobility, Handover, Networks, Security","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministère des Ressources Naturelles et de la Faune","keywords":"Multihoming; Handover; Computer network; Computer science; Router; Mobile IP; Network packet; Routing protocol; Routing (electronic design automation); Throughput; Mobility management; Distributed computing; The Internet; Internet Protocol; Wireless; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005963356,0.0006122491,0.000502597,0.0006441735,0.0005176843,0.0004094861,0.001048315,0.0005025882,0.002001923],"category_scores_gemma":[0.001117134,0.0001275879,0.0002936574,0.000349092,0.0001802138,0.0009522525,0.0007794846,0.0005878953,0.0005830865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003556225,"about_ca_system_score_gemma":0.0003186137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004747626,"about_ca_topic_score_gemma":0.0007534795,"domain_scores_codex":[0.9994824,0.00009026409,0.00005378552,0.00008780667,0.0001634629,0.0001222429],"domain_scores_gemma":[0.9993619,0.0001253134,0.00009392299,0.0001740373,0.0001960875,0.0000487418],"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":[0.001125889,0.0005949786,0.002878543,0.0005981639,0.0001192868,0.0005494058,0.0003180648,0.03320695,0.4268902,0.01521077,0.00715738,0.5113504],"study_design_scores_gemma":[0.0003896858,0.005604664,0.009489544,0.000139128,0.0003426123,0.003883469,0.0003222685,0.6035921,0.3132363,0.00653677,0.05620665,0.0002567269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3993909,0.006794239,0.5683634,0.0006890559,0.0007669037,0.0007125566,0.0004060714,0.006235743,0.01664118],"genre_scores_gemma":[0.8627135,0.0009433483,0.1308788,0.0002414339,0.0001337369,0.0001528316,0.0003203422,0.00007828433,0.004537656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002001923,"threshold_uncertainty_score":0.006697059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007197987789501192,"score_gpt":0.2392746204787338,"score_spread":0.2320766326892326,"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."}}