{"id":"W2059807221","doi":"10.1109/glocom.2011.6133624","title":"Interference Analysis of Co-Existing Wireless Body Area Networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Interference (communication); Computer science; Signal-to-interference-plus-noise ratio; Wireless network; Wireless; Network performance; Co-channel interference; Computer network; Network planning and design; Signal-to-noise ratio (imaging); Telecommunications; Channel (broadcasting); Power (physics)","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":[0.001185625,0.0008555423,0.0005819142,0.001303016,0.0004871117,0.0007301682,0.001268354,0.0008499877,0.001135021],"category_scores_gemma":[0.005047718,0.000355443,0.0007827522,0.001226514,0.001161889,0.001247335,0.001162334,0.0005827833,0.0003108676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001090405,"about_ca_system_score_gemma":0.0005035931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003294546,"about_ca_topic_score_gemma":0.001725773,"domain_scores_codex":[0.9986768,0.0004253276,0.0000480173,0.0001406581,0.0005133937,0.0001958201],"domain_scores_gemma":[0.9965417,0.002249904,0.0003679239,0.0001835236,0.0005693826,0.0000875707],"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":[0.00004414722,0.000021959,0.002004713,0.00004403923,0.00005518239,0.0003384864,0.00008036237,0.9654677,0.002450823,0.02113534,0.0003205055,0.008036694],"study_design_scores_gemma":[0.000001673464,0.00001577949,0.0004812858,0.000004195972,0.00000904948,0.0001237668,0.00002358833,0.9949946,0.0003647231,0.003732471,0.0002439578,0.000004845298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09788869,0.001422892,0.8887438,0.000207065,0.00005678408,0.00004740246,0.00006443987,0.0001326965,0.01143631],"genre_scores_gemma":[0.9744025,0.0009563329,0.02222108,0.00007509927,0.00005942331,0.00006238601,0.00006902145,0.00003097304,0.00212309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003294546,"threshold_uncertainty_score":0.007911503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03612139535870686,"score_gpt":0.2379990488424605,"score_spread":0.2018776534837536,"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."}}