{"id":"W2263131770","doi":"","title":"Energy Efficiency Analysis for LTE Macro-Femto HetNets","year":2013,"lang":"en","type":"article","venue":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Femtocell; Femto-; Computer science; Heterogeneous network; Macro; Reuse; Computer network; Bandwidth (computing); Interference (communication); Energy consumption; Channel (broadcasting); Wireless; Wireless network; Telecommunications; Engineering; Base station; Electrical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002343741,0.000291767,0.000247493,0.0002515189,0.0002194299,0.0003493667,0.0002309954,0.000241381,0.001119469],"category_scores_gemma":[0.0006159411,0.0001243337,0.0002821753,0.0003128514,0.0002547508,0.0003813559,0.0002119733,0.000126497,0.000156771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006741346,"about_ca_system_score_gemma":0.0002634861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003709476,"about_ca_topic_score_gemma":0.003246283,"domain_scores_codex":[0.9998471,0.0000460876,0.000003772127,0.00001593526,0.00004626228,0.00004081647],"domain_scores_gemma":[0.9998319,0.0001090004,0.00001277061,0.00001297897,0.00002751995,0.00000582631],"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":[0.00004653657,0.00001077632,0.001337862,0.00001148348,0.00001402787,0.00004735374,0.00001571617,0.988001,0.002486622,0.004296354,0.0001629249,0.003569228],"study_design_scores_gemma":[0.000001692438,0.00002229327,0.001094949,0.000002174274,0.000005539625,0.0000235619,0.00001719061,0.9965943,0.001031702,0.0008857591,0.0003183652,0.000002403734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7362602,0.001060914,0.2343022,0.0002469691,0.00003131717,0.00003617619,0.000207007,0.0001494056,0.02770592],"genre_scores_gemma":[0.9966424,0.0001340488,0.002025494,0.00001809113,0.000003033484,0.00001041094,0.00004505158,0.00001202052,0.001109414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003709476,"threshold_uncertainty_score":0.007375777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007982201947579539,"score_gpt":0.2124620442425024,"score_spread":0.2044798422949228,"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."}}