{"id":"W1996618820","doi":"10.1109/icc.2012.6364427","title":"Hierarchical resource allocation in femtocell networks using graph algorithms","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Femtocell; Computer science; Resource allocation; Distributed computing; Computer network; Interference (communication); Resource management (computing); Load balancing (electrical power); Graph; Channel allocation schemes; Channel (broadcasting); Wireless; Theoretical computer science; Mathematics; Telecommunications","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":[0.0006272508,0.00089566,0.0009326391,0.0009508246,0.0006067707,0.0008468961,0.0009087806,0.0006997275,0.00218068],"category_scores_gemma":[0.001549525,0.0003935544,0.0005713946,0.001209798,0.0007665407,0.00107538,0.0008624594,0.0006703722,0.0003432475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001513857,"about_ca_system_score_gemma":0.001278177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0094264,"about_ca_topic_score_gemma":0.01097951,"domain_scores_codex":[0.9995186,0.0001944817,0.00001253919,0.00008700653,0.0001084206,0.00007897316],"domain_scores_gemma":[0.9993768,0.0004189918,0.00005624802,0.00005266103,0.00006066973,0.0000346281],"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":[0.00002477309,0.00002773614,0.0001659472,0.00002702877,0.00001799116,0.00001966558,0.00002269644,0.9597241,0.0005724762,0.01383448,0.0008421775,0.02472085],"study_design_scores_gemma":[0.000005822042,0.000005923914,0.00003929135,0.000001975958,0.000002393291,0.000004435386,0.000006112391,0.9892504,0.0001167311,0.01026132,0.0003030979,0.000002478371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01337418,0.0002620556,0.9825654,0.0001489033,0.00002354945,0.0000496361,0.00006369902,0.0003082941,0.003204332],"genre_scores_gemma":[0.5373744,0.0006075142,0.4571866,0.0001688845,0.00006242789,0.0001895555,0.0002749766,0.0001294315,0.004006171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0094264,"threshold_uncertainty_score":0.0187431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01601281467247252,"score_gpt":0.2353899006460718,"score_spread":0.2193770859735993,"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."}}