{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008290397,0.0004207218,0.0005733717,0.0001931853,0.0002898589,0.0006992882,0.0002743555,0.000209168,0.000008536173],"category_scores_gemma":[0.0002533311,0.0003947949,0.0001597667,0.0009689401,0.0001904944,0.0008478205,0.0001772977,0.000418145,0.00001418449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002577108,"about_ca_system_score_gemma":0.0002913548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002076921,"about_ca_topic_score_gemma":0.000842669,"domain_scores_codex":[0.996921,0.0002357561,0.000542182,0.0009886172,0.0003422189,0.0009701944],"domain_scores_gemma":[0.9975834,0.0008794693,0.0001423392,0.000362117,0.0007180832,0.0003145268],"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":[0.00140302,0.0006139209,0.00565571,0.00007438398,0.0005993916,0.002458815,0.02181178,0.1624489,0.0004842542,0.03052675,0.01566705,0.758256],"study_design_scores_gemma":[0.003092814,0.000260662,0.001130283,0.0001671117,0.00001898509,0.0002644814,0.0005369995,0.9910268,0.001033764,0.001024452,0.0008477304,0.0005959173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05862339,0.0003099512,0.934444,0.0007640343,0.0008022538,0.0008051093,0.000006553088,0.0002526466,0.003992134],"genre_scores_gemma":[0.971119,0.00001961118,0.02636969,0.001314134,0.0006066771,0.000004324772,0.00002113923,0.00004487435,0.0005005798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9124956,"threshold_uncertainty_score":0.9998504,"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."}}