{"id":"W1566257247","doi":"10.1109/jsac.2015.2435391","title":"HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless Cellular Networks","year":2015,"lang":"en","type":"preprint","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Heterogeneous network; Computer science; Poisson distribution; Base station; Wireless network; Spatial correlation; Cellular network; Moment (physics); Spatial heterogeneity; User equipment; Wireless; Computer network; Statistics; Telecommunications; Mathematics","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006622261,0.00059458,0.0007746019,0.0005394138,0.0001866016,0.0001838966,0.001653267,0.0007639822,0.000008769571],"category_scores_gemma":[0.000108299,0.0006828094,0.0001658155,0.0009968519,0.00009083328,0.0001629194,0.0002401088,0.003938882,0.00001457956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002692797,"about_ca_system_score_gemma":0.0002348456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002622459,"about_ca_topic_score_gemma":0.0008460346,"domain_scores_codex":[0.9963496,0.000809029,0.001451749,0.0004023156,0.0003435572,0.0006437376],"domain_scores_gemma":[0.9965006,0.0002597894,0.0004729869,0.001994562,0.0005152025,0.00025692],"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":[0.00003300537,0.0002261549,0.0001041082,0.00004060391,0.00007940352,0.00005943211,0.0002900521,0.9958487,0.0001521253,0.00001330114,0.0003236651,0.002829467],"study_design_scores_gemma":[0.0007343019,0.00006388724,0.00004897248,0.001015777,0.0000369745,0.0003067195,0.00003172485,0.9958327,0.0004188328,0.000263061,0.0006346222,0.0006124578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6433154,0.01715931,0.3328465,0.0002512119,0.003035975,0.00187289,0.0002844987,0.0008656913,0.0003686007],"genre_scores_gemma":[0.9903044,0.006592446,0.001281808,0.00002351428,0.0002626681,0.0001980248,0.001163397,0.0001599752,0.00001383635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.346989,"threshold_uncertainty_score":0.9995623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0258876139237047,"score_gpt":0.2655454055180485,"score_spread":0.2396577915943438,"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."}}