{"id":"W2148393683","doi":"10.1109/tvt.2007.891403","title":"Optimization of Sequential Paging in Movement-Based Location Management Based on Movement Statistics","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Paging; Computer science; Scheme (mathematics); Range (aeronautics); Interval (graph theory); Poisson distribution; Movement (music); Boundary (topology); Real-time computing; Algorithm; Statistics; Mathematics; Computer network; Engineering","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":[0.001366323,0.0005819605,0.0009368146,0.0005358896,0.0004138454,0.0007141979,0.001085025,0.0004792817,0.0006346863],"category_scores_gemma":[0.006371065,0.0004455659,0.00026597,0.0007709402,0.0006898055,0.001563707,0.0008699992,0.0004865626,0.0001628229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001041296,"about_ca_system_score_gemma":0.001249328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002493725,"about_ca_topic_score_gemma":0.002195098,"domain_scores_codex":[0.998753,0.0004611905,0.0000958596,0.0002274915,0.0002834598,0.0001789969],"domain_scores_gemma":[0.9964315,0.002074165,0.000488263,0.0003992935,0.0004581831,0.0001485296],"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":[0.0002628978,0.00008924794,0.00230357,0.00008541688,0.00003545511,0.0001212029,0.0001036929,0.9005541,0.008335584,0.01235413,0.001197498,0.07455721],"study_design_scores_gemma":[0.00001068276,0.00006867458,0.0003562744,0.000002830341,0.000008024948,0.00005037649,0.00001505071,0.9956385,0.001021315,0.002550511,0.0002714925,0.000006221535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08904349,0.0003057741,0.9084632,0.0001831755,0.0000368446,0.0000733384,0.00004389627,0.0005173421,0.001333023],"genre_scores_gemma":[0.9477888,0.0001046872,0.0513848,0.00002778193,0.00002286367,0.00004905382,0.00004452596,0.00002671459,0.0005508588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002493725,"threshold_uncertainty_score":0.007555127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01410756577436616,"score_gpt":0.2694764762681739,"score_spread":0.2553689104938078,"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."}}