{"id":"W2896913059","doi":"10.1109/tmc.2018.2865340","title":"On Mutual Interference Analysis in Hybrid Interweave-Underlay Cognitive Communications","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Underlay; Computer science; Interference (communication); Computer network; Cognitive radio; Telecommunications; Wireless; Signal-to-noise ratio (imaging); Channel (broadcasting)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002075237,0.001474834,0.0008735745,0.001130209,0.0006343636,0.001526799,0.001493344,0.00107616,0.001395652],"category_scores_gemma":[0.005949546,0.0004704171,0.0009022183,0.001079882,0.001869485,0.00250998,0.001828591,0.001199238,0.0002542883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0015295,"about_ca_system_score_gemma":0.000994126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003673218,"about_ca_topic_score_gemma":0.002355771,"domain_scores_codex":[0.9981036,0.0005899178,0.00005949187,0.0002087902,0.0007210969,0.0003171458],"domain_scores_gemma":[0.9957158,0.00299634,0.0005093629,0.000247332,0.0004413995,0.00008973554],"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":[0.00006391029,0.00003977188,0.000661835,0.00008739418,0.00008088477,0.0003096818,0.0001662769,0.9285398,0.003610491,0.058657,0.0002600069,0.007522879],"study_design_scores_gemma":[0.000003510935,0.00002593713,0.0002264745,0.000008597146,0.00001577939,0.00008035955,0.00002880234,0.9849786,0.0006000357,0.01379078,0.000229725,0.00001141768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04394722,0.0009462605,0.9486696,0.0001644929,0.00003326486,0.00002719259,0.00004005909,0.0001070999,0.006064925],"genre_scores_gemma":[0.9686558,0.000891263,0.02793758,0.0001403412,0.00007706669,0.00006307913,0.000035095,0.00005293328,0.002146855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003673218,"threshold_uncertainty_score":0.01109737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02225331465431639,"score_gpt":0.2880059926411514,"score_spread":0.265752677986835,"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."}}