{"id":"W2078504141","doi":"10.1109/lcn.2005.11","title":"A mobile terminal location tracking model for personal communication systems","year":2005,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Atlantic Canada Opportunities Agency","keywords":"Computer science; Terminal (telecommunication); Markov decision process; Dynamic programming; Markov process; Hidden Markov model; Wireless; Markov chain; Path (computing); Sample (material); Real-time computing; Partially observable Markov decision process; Markovian arrival process; Tracking (education); Markov model; Computer network; Algorithm; Artificial intelligence; Machine learning; Mathematics; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007037309,0.00009663337,0.0001138922,0.0001031086,0.0003000926,0.0003381819,0.001511025,0.00006509644,0.000007176283],"category_scores_gemma":[0.0000265061,0.00009378419,0.00004666128,0.0002918412,0.00004845647,0.0009169458,0.0002884672,0.0001577249,0.0000309879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001438199,"about_ca_system_score_gemma":0.000128595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002021129,"about_ca_topic_score_gemma":0.00002738788,"domain_scores_codex":[0.9988424,0.0001165566,0.000263965,0.0002350109,0.0002900856,0.0002519761],"domain_scores_gemma":[0.9982805,0.0002189604,0.00009073575,0.0009279607,0.0004072227,0.00007461363],"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":[0.00001597852,0.0002294055,0.00004559051,0.00005203385,0.00001883865,2.267242e-7,0.007801679,0.5051178,0.0003541637,0.126226,0.005643458,0.3544948],"study_design_scores_gemma":[0.0002306508,0.00003154923,0.00003683209,0.00003152668,0.000002032757,0.00001001575,0.000107987,0.9956419,0.00007055585,0.000223409,0.003502458,0.000111125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002697474,0.002452815,0.99214,0.0009078759,0.00002788629,0.0006126637,0.000001498931,0.0001798451,0.0009799356],"genre_scores_gemma":[0.854929,0.0001242645,0.1422262,0.0001444566,0.00005606467,0.0005994202,0.00001380737,0.00001009006,0.001896657],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8522316,"threshold_uncertainty_score":0.3824408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06402719947706398,"score_gpt":0.3346222560963505,"score_spread":0.2705950566192865,"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."}}