{"id":"W2329733637","doi":"10.1109/jsyst.2014.2305871","title":"Pattern-Search-Based Nonconvex Cooperative Sensing in Multiband Cognitive Radio Systems","year":2014,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Mathematical optimization; Computer science; Throughput; Set (abstract data type); Convexity; Interference (communication); False alarm; Algorithm; Mathematics; Wireless; Artificial intelligence; Computer network","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":[0.0008230095,0.0006990453,0.0009727988,0.00042696,0.0003447398,0.0007567814,0.001114445,0.001036477,0.0008217844],"category_scores_gemma":[0.002230053,0.0004099604,0.000534691,0.0007488767,0.0007702774,0.000809997,0.0009360523,0.0005540749,0.0001605342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006147657,"about_ca_system_score_gemma":0.001002663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003594415,"about_ca_topic_score_gemma":0.002584667,"domain_scores_codex":[0.9994824,0.0001639325,0.00002180751,0.0001062428,0.0001545339,0.00007103603],"domain_scores_gemma":[0.9991935,0.0004783748,0.0001098049,0.00005855173,0.0001266628,0.00003309287],"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":[0.00003232819,0.00002349917,0.0001740618,0.00003567433,0.00002244838,0.00006281451,0.0000342949,0.9752983,0.001307142,0.0059563,0.0003148421,0.01673831],"study_design_scores_gemma":[0.000005833003,0.00001791142,0.00003924971,0.00000200472,0.000002418203,0.00001167954,0.000004665254,0.9973339,0.0002487315,0.002184865,0.0001463563,0.000002373569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01475939,0.0001638849,0.9829828,0.0001431369,0.00002243452,0.00003272963,0.00001650593,0.00006854861,0.001810614],"genre_scores_gemma":[0.8352527,0.0002871231,0.1601604,0.0001822227,0.00004139913,0.0002080248,0.00006300597,0.00004508751,0.003760038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003594415,"threshold_uncertainty_score":0.007147014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02025984297087295,"score_gpt":0.2533902521713681,"score_spread":0.2331304092004952,"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."}}