{"id":"W4301228018","doi":"10.48550/arxiv.1505.00076","title":"HetHetNets: Heterogeneous Traffic Distribution in Heterogeneous Wireless\\n Cellular Networks","year":2015,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005224954,0.0006321893,0.0004129447,0.0005154504,0.0003508287,0.0009809704,0.0007692509,0.0005879538,0.000759499],"category_scores_gemma":[0.001327991,0.0002459363,0.0004434509,0.001041787,0.0006319282,0.001087498,0.0006270556,0.00049399,0.0002401313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008206395,"about_ca_system_score_gemma":0.000404267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00636788,"about_ca_topic_score_gemma":0.005156692,"domain_scores_codex":[0.9995902,0.0001406453,0.00001412864,0.0001060943,0.00008610482,0.00006274004],"domain_scores_gemma":[0.9996339,0.0001714833,0.00006640737,0.00004680767,0.00006410645,0.00001726139],"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":[0.0000459748,0.00002384407,0.002214815,0.00003344936,0.00002887436,0.0002899543,0.00008673879,0.8721192,0.003226067,0.1031404,0.001192599,0.01759799],"study_design_scores_gemma":[0.000001525235,0.000006465944,0.000345829,0.000002278144,0.000004209465,0.00003360172,0.00001799255,0.9910792,0.0003080128,0.007566874,0.0006299288,0.000004130484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05375405,0.0002925355,0.9417295,0.0001547205,0.00005563886,0.00003216435,0.0002205806,0.0001839969,0.003576908],"genre_scores_gemma":[0.9554827,0.0008483342,0.03654413,0.0001139887,0.00009288397,0.00009384112,0.0003381604,0.00005164484,0.006434315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00636788,"threshold_uncertainty_score":0.01266164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07293555996753726,"score_gpt":0.1710869317154328,"score_spread":0.09815137174789554,"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."}}