{"id":"W2108293981","doi":"10.1145/774749.774759","title":"Efficient micro-mobility using intra-domain multicast-based mechanisms (M&M)","year":2002,"lang":"en","type":"article","venue":"ACM SIGCOMM Computer Communication Review","topic":"IPv6, Mobility, Handover, Networks, Security","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Handover; Computer network; Multicast; Mobility management; Proxy Mobile IPv6; Mobile IP; Node (physics); Network packet; Scalability; Inter-domain; Mobility model; Packet loss; Distributed computing; Source-specific multicast","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":[0.00117819,0.000402355,0.0003264855,0.0006302215,0.0003368404,0.0005296902,0.0007700947,0.0004958442,0.000461103],"category_scores_gemma":[0.001352835,0.0001484306,0.0002145436,0.0004723384,0.0003311577,0.0009970593,0.0009131199,0.0003878697,0.0001492111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003525362,"about_ca_system_score_gemma":0.0003252953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000495463,"about_ca_topic_score_gemma":0.0005756856,"domain_scores_codex":[0.9995235,0.0001231095,0.00003091398,0.00005146439,0.0002036447,0.00006738069],"domain_scores_gemma":[0.9991949,0.000254636,0.000157187,0.0002234101,0.0001270149,0.00004280602],"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":[0.0005691756,0.0002992888,0.01074977,0.0005649809,0.0001803205,0.0003177268,0.0003449207,0.08969618,0.336945,0.03135011,0.003487701,0.5254949],"study_design_scores_gemma":[0.0001305752,0.00220407,0.0151246,0.00009749189,0.0001962438,0.002037413,0.0004468521,0.6530346,0.2795877,0.01076074,0.03624258,0.0001371717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.27593,0.002720349,0.7066754,0.0003766809,0.0001050991,0.0004300158,0.0001830506,0.002463289,0.01111617],"genre_scores_gemma":[0.8729883,0.0003950899,0.1252978,0.00005493698,0.00002175249,0.0001210882,0.0001118169,0.00002124888,0.0009880589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00117819,"threshold_uncertainty_score":0.00623095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04355580001479402,"score_gpt":0.2667041256848755,"score_spread":0.2231483256700815,"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."}}