{"id":"W4252336338","doi":"10.1002/wcm.745","title":"Markov mobility model and registration area optimization in cellular networks","year":2009,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nortel (Canada); Université de Moncton","funders":"","keywords":"Computer science; Markov chain; Cellular network; Mobility model; Cluster analysis; Markov model; Real-time computing; Computer network; Task (project management); Artificial intelligence; Machine learning","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":[0.001180748,0.000196989,0.0002649403,0.0001850738,0.0005663654,0.0003469416,0.001665237,0.0001363597,0.000001318078],"category_scores_gemma":[0.00003102201,0.0002162427,0.0000362459,0.0006797501,0.000205595,0.0005300645,0.001277043,0.0005152207,4.44487e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008183702,"about_ca_system_score_gemma":0.00007012818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004488283,"about_ca_topic_score_gemma":0.00004178321,"domain_scores_codex":[0.9980239,0.0003898242,0.0005524108,0.0004748802,0.000207591,0.0003513728],"domain_scores_gemma":[0.9964865,0.0004129289,0.0002023906,0.002599541,0.0001693646,0.0001292973],"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":[0.000004835495,0.0001726336,0.0005593579,0.000009098743,0.000004240579,8.304719e-7,0.0005768415,0.7632958,0.000180198,0.01640915,0.00002591593,0.2187611],"study_design_scores_gemma":[0.0003284597,0.00005559176,0.001994742,0.00008336065,0.000003149313,0.000006969854,0.00007959805,0.996377,0.00001449344,0.0007605536,0.00009047183,0.0002056152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1153003,0.003171206,0.8787996,0.001163976,0.0000216782,0.0005239859,9.81618e-7,0.0001461527,0.0008722142],"genre_scores_gemma":[0.8698273,0.003892879,0.1260297,0.0001211336,0.00001430173,0.00005001744,0.0000320613,0.00001016829,0.00002241099],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7545271,"threshold_uncertainty_score":0.8818119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02771240212597911,"score_gpt":0.282081722646065,"score_spread":0.2543693205200859,"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."}}