{"id":"W3105907636","doi":"","title":"1Asymptotic Scheduling Gains in Point-to-Multipoint Cognitive Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scheduling (production processes); Telecommunications link; Lemma (botany); Channel (broadcasting); Topology (electrical circuits); Interference (communication); Asymptotic analysis; Computer science; Cognitive radio; Mathematics; Mathematical optimization; Computer network; Telecommunications; Combinatorics; Wireless; Mathematical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.0004456802,0.0002180675,0.0002580292,0.0002475049,0.00009751089,0.0001383554,0.0003647335,0.00007803704,0.00004603786],"category_scores_gemma":[0.0002245853,0.0001536281,0.00008488385,0.000730202,0.00004847286,0.0004580375,0.0002916549,0.0001684819,0.0001733898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001292293,"about_ca_system_score_gemma":0.00004994583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004639919,"about_ca_topic_score_gemma":0.0003500342,"domain_scores_codex":[0.9980072,0.0001099332,0.0003320413,0.0006439447,0.000212395,0.0006945246],"domain_scores_gemma":[0.9985555,0.0007190173,0.00005985273,0.0003129615,0.0001210387,0.0002316615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007126474,0.000179876,0.01089587,0.000006250027,0.00005449786,0.0003016613,0.001044742,0.006657311,0.001742776,0.0539765,0.0002369884,0.9248323],"study_design_scores_gemma":[0.002216484,0.000214548,0.03467001,0.0009557732,0.000009458822,0.00007023813,0.0001808165,0.9558568,0.001904033,0.00302619,0.0001523726,0.000743277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09102762,0.00007080573,0.9001738,0.002800899,0.0002864378,0.0003034913,7.304866e-7,0.0001869299,0.005149276],"genre_scores_gemma":[0.9756095,0.0000350005,0.02215007,0.001763278,0.0002032723,0.00001092975,6.08374e-7,0.00001804104,0.0002093398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9491995,"threshold_uncertainty_score":0.6264772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01792891832978115,"score_gpt":0.2556223548333506,"score_spread":0.2376934365035694,"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."}}