{"id":"W4238182125","doi":"10.32920/ryerson.14654724.v1","title":"An Optimal Initial Radio Access Technology Selection Method for Heterogeneous Wireless Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Markov decision process; Blocking (statistics); Computer network; Selection (genetic algorithm); Radio access technology; Wireless; Wireless network; Energy consumption; Heterogeneous network; Service (business); Radio resource management; Markov process; Telecommunications; Base station; Engineering; User equipment; 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":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007268334,0.0005573248,0.0008293404,0.0003370652,0.0003076557,0.002083237,0.003680117,0.001429516,0.00005677266],"category_scores_gemma":[0.00001673525,0.0005601856,0.0003069389,0.0007882067,0.00006156655,0.0006292818,0.002566016,0.001137395,0.000001363354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001332986,"about_ca_system_score_gemma":0.0004535704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008544519,"about_ca_topic_score_gemma":0.0001279017,"domain_scores_codex":[0.9961417,0.0003689532,0.0006353115,0.001753444,0.0002843726,0.000816195],"domain_scores_gemma":[0.9973285,0.0001964548,0.0003825709,0.001421962,0.0004699619,0.0002005141],"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":[0.00005311105,0.0001126736,0.00005580409,0.00007963784,0.0001108938,0.00003187733,0.00005356547,0.7002544,0.00006831849,0.003435461,0.0003210302,0.2954232],"study_design_scores_gemma":[0.0004353579,0.0003126459,0.00001768724,0.0001063265,0.00003024975,0.000180139,0.000009139136,0.9837497,0.01155508,0.001895115,0.001053807,0.0006547583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002938256,0.0002021438,0.985916,0.0004343468,0.001431658,0.007945661,0.000006699659,0.001004428,0.0001208457],"genre_scores_gemma":[0.2546498,0.00004887567,0.7250384,0.0004038025,0.00143191,0.01820512,0.00009962387,0.0000743554,0.00004810422],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2947685,"threshold_uncertainty_score":0.9998668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03430120853258255,"score_gpt":0.371978863440774,"score_spread":0.3376776549081914,"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."}}