{"id":"W2286771541","doi":"10.1109/vtcfall.2015.7391128","title":"Secondary User Interference Characterization for Underlay Networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Underlay; Rayleigh fading; Computer science; Interference (communication); Computer network; Transmitter; Topology (electrical circuits); Fading; Telecommunications; Electronic engineering; Signal-to-noise ratio (imaging); Engineering; Electrical engineering; Channel (broadcasting)","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":[0.0007252538,0.0006998436,0.0008597746,0.000836818,0.0004827147,0.001176855,0.0009928164,0.0005643207,0.001275186],"category_scores_gemma":[0.004725466,0.000244942,0.000484202,0.0008103974,0.0006432444,0.00104861,0.001155961,0.0006189918,0.0002423553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007741876,"about_ca_system_score_gemma":0.000388444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002066624,"about_ca_topic_score_gemma":0.001143896,"domain_scores_codex":[0.9991811,0.0001813845,0.00004353798,0.0001506491,0.0002931962,0.0001500346],"domain_scores_gemma":[0.9972814,0.001566316,0.0003604403,0.0002128217,0.0004653838,0.0001134596],"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":[0.0003939845,0.0001096831,0.0169775,0.0001903861,0.000148579,0.001120924,0.0009168539,0.8265954,0.05092313,0.06501332,0.001066113,0.03654402],"study_design_scores_gemma":[0.000005629405,0.00008033328,0.00384345,0.00001038533,0.00002346033,0.0004853115,0.0001149783,0.9806194,0.003677585,0.01031096,0.000814819,0.0000136052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3801921,0.001115483,0.6105268,0.00009568965,0.00002271019,0.00005211356,0.0002044428,0.0001629374,0.00762772],"genre_scores_gemma":[0.9903235,0.0002763485,0.008465129,0.00002371921,0.00002681735,0.00003474635,0.00008045469,0.00001683112,0.0007523999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002066624,"threshold_uncertainty_score":0.005617142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03074669791777984,"score_gpt":0.2482195005510885,"score_spread":0.2174728026333087,"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."}}