{"id":"W1976275892","doi":"10.5539/nct.v1n1p67","title":"Dynamic Radio Resource Allocation for Macro-Femto Hybrid Cellular Network Maintaining Fairness","year":2012,"lang":"en","type":"article","venue":"Network and Communication Technologies","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Computer network; Femtocell; Femto-; Quality of service; Throughput; Resource allocation; Macro; Transmission (telecommunications); Cellular network; Wireless; Base station; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008382046,0.0003264961,0.0005983901,0.0003017834,0.0005481656,0.0007054523,0.0008154362,0.0005034123,0.0007712558],"category_scores_gemma":[0.001817982,0.0001671482,0.0001567223,0.0004626031,0.000577284,0.0005912157,0.0004624404,0.0003137459,0.0001148037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001237614,"about_ca_system_score_gemma":0.001017965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00457988,"about_ca_topic_score_gemma":0.004796196,"domain_scores_codex":[0.9995122,0.000175716,0.00001324236,0.00008012821,0.0001008708,0.0001178213],"domain_scores_gemma":[0.9993883,0.0003485977,0.00007263849,0.00004327848,0.0001011612,0.00004598797],"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":[0.000113409,0.00004536525,0.0006883808,0.00002555865,0.00002078914,0.00007060255,0.00004240937,0.9615097,0.004079612,0.01392851,0.0006462911,0.01882933],"study_design_scores_gemma":[0.000003923442,0.00001644972,0.00008068216,0.000001095452,0.000002729608,0.00001455961,0.000005999274,0.9978057,0.0002530859,0.00165993,0.0001531001,0.000002774358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1309718,0.0005082362,0.8637508,0.0002032765,0.00006377517,0.00005677249,0.00004005444,0.0001389263,0.004266282],"genre_scores_gemma":[0.9810131,0.00008773245,0.01799934,0.00003924937,0.00001868835,0.00002759408,0.000008497034,0.00000903315,0.0007966982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00457988,"threshold_uncertainty_score":0.009106457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007231940679929581,"score_gpt":0.2105241338257588,"score_spread":0.2032921931458292,"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."}}