{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006966978,0.0001607097,0.0001725845,0.001646887,0.000122294,0.00003167495,0.0008496701,0.000161243,0.00002162232],"category_scores_gemma":[0.000007360014,0.0001815189,0.00004387819,0.002053825,0.0001134457,0.0001061016,0.00001206923,0.0003836497,0.000008677269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003473648,"about_ca_system_score_gemma":0.00007738448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003517139,"about_ca_topic_score_gemma":0.00008878772,"domain_scores_codex":[0.9981465,0.0001129761,0.000477629,0.0003795697,0.0005332408,0.0003500386],"domain_scores_gemma":[0.9983422,0.0001303402,0.0001439969,0.001137859,0.0001976188,0.00004797631],"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":[0.00003289522,0.0005102232,0.00004801577,0.00003563652,0.0000196832,0.00001674921,0.00001836277,0.9240883,0.0007924431,0.01102242,0.00001129103,0.06340396],"study_design_scores_gemma":[0.0009273681,0.0001796615,0.0001533427,0.00009996341,0.000008162066,3.250386e-7,0.00003631655,0.9179117,0.07994031,0.0005684235,0.00004078386,0.0001336193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002176133,0.00001945251,0.9955227,0.001269499,0.0001315955,0.0005423017,0.000006399965,0.0001861241,0.0001457288],"genre_scores_gemma":[0.7798832,0.00002602208,0.2196071,0.0003433942,0.000003571544,0.00009284474,0.00001005453,0.00001419345,0.00001964799],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.777707,"threshold_uncertainty_score":0.7402127,"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."}}