{"id":"W4282828886","doi":"10.1016/j.asoc.2022.109152","title":"Artificial Bee optimization aided joint user association and resource allocation in HCRAN","year":2022,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Heterogeneous network; Resource allocation; Radio access network; Cloud computing; Distributed computing; Cellular network; Orthogonal frequency-division multiplexing; Efficient energy use; Base station; Baseband; Joint (building); Energy consumption; Computer network; Wireless network; Wireless; Mobile station; Telecommunications; Bandwidth (computing); 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":[0.0005466489,0.0001247164,0.0001706016,0.0001570336,0.0002147056,0.00004092853,0.00006668288,0.0000633312,0.00001272422],"category_scores_gemma":[0.00005172866,0.0001728902,0.00001751545,0.0003717795,0.000007397851,0.00008804406,0.00007984722,0.0002290003,0.000003217227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005290897,"about_ca_system_score_gemma":0.0000113206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001110504,"about_ca_topic_score_gemma":0.000009373924,"domain_scores_codex":[0.9989122,0.00005368749,0.0004010614,0.000224373,0.0001849876,0.0002236741],"domain_scores_gemma":[0.9996024,0.0001101951,0.0001336151,0.00009757972,0.00002455697,0.00003168494],"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":[0.000006174468,0.00001234274,0.0003356819,0.00002094611,0.000008768398,6.194795e-7,0.0009818048,0.9886775,0.00460158,0.0005679337,0.000138105,0.004648566],"study_design_scores_gemma":[0.0002750139,0.000008802525,0.0003753362,0.00001229668,0.000006975493,0.000002048221,0.0005298526,0.9973765,0.0007303092,0.0001641385,0.0003423898,0.0001763182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08865571,0.0000531208,0.9093663,0.00008631484,0.0001398765,0.0004235197,0.00000267568,0.0003801732,0.0008922938],"genre_scores_gemma":[0.9771934,0.000002945996,0.02246783,0.00006906877,0.00008260927,0.00004372033,0.00007182178,0.00004453096,0.00002406285],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8885377,"threshold_uncertainty_score":0.7050256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008901281676998385,"score_gpt":0.1976590600975877,"score_spread":0.1887577784205893,"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."}}