{"id":"W1572171790","doi":"10.1109/icc.2015.7249519","title":"A cross-layer aware sensing-throughput tradeoff in cooperative sensing for cognitive radio networks","year":2015,"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":"Université de Montréal","funders":"","keywords":"Cognitive radio; Throughput; Computer science; Interference (communication); Constraint (computer-aided design); Signal-to-noise ratio (imaging); Physical layer; Context (archaeology); Imperfect; Computer network; Channel (broadcasting); Telecommunications; Wireless; Engineering","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":[0.003288843,0.001116208,0.0008949767,0.0006010116,0.0005777577,0.001841998,0.0009824028,0.001138631,0.0009470761],"category_scores_gemma":[0.008512675,0.0006231857,0.0005549473,0.0007312162,0.001136077,0.002146765,0.001888962,0.0008014399,0.0001215851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001154301,"about_ca_system_score_gemma":0.0009236421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001338939,"about_ca_topic_score_gemma":0.001279447,"domain_scores_codex":[0.9986253,0.0006250213,0.00005707407,0.0002130414,0.0002872053,0.0001924019],"domain_scores_gemma":[0.9969341,0.002299909,0.000234893,0.0001643207,0.0002862012,0.00008064352],"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":[0.0001339875,0.00007116918,0.0008073854,0.0001377482,0.00009987251,0.0001990357,0.0002033408,0.9302115,0.01162895,0.030796,0.0005203564,0.02519055],"study_design_scores_gemma":[0.000003832799,0.00003919819,0.0001319997,0.0000062682,0.000019468,0.00004903284,0.00002008956,0.993611,0.0009790253,0.005029318,0.00009976415,0.00001099567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07268005,0.001535691,0.9221222,0.0002723959,0.00005806839,0.00003515743,0.00002485612,0.0001117736,0.003159843],"genre_scores_gemma":[0.9710125,0.0004611569,0.02780352,0.00007583961,0.00004300646,0.00003372791,0.00001044013,0.00002068525,0.0005391075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003288843,"threshold_uncertainty_score":0.01739323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04662914817830216,"score_gpt":0.3058067026070409,"score_spread":0.2591775544287387,"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."}}