{"id":"W2809804643","doi":"10.3390/jsan7030025","title":"Fundamental Limitations in Energy Detection for Spectrum Sensing","year":2018,"lang":"en","type":"article","venue":"Journal of Sensor and Actuator Networks","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; False alarm; Constant false alarm rate; Cognitive radio; Energy (signal processing); Detection theory; Key (lock); Statistical power; Detector; Artificial intelligence; Real-time computing; Telecommunications; Wireless; Computer security; Statistics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002797268,0.0001195972,0.0002122592,0.0001970973,0.0001767161,0.0001665935,0.00009714952,0.00006828108,0.000001720452],"category_scores_gemma":[0.00004253343,0.0001075217,0.00008811864,0.0002755376,0.00006429115,0.0003347243,0.00003335816,0.0001658262,3.900956e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006854938,"about_ca_system_score_gemma":0.00002705321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000100988,"about_ca_topic_score_gemma":0.0002789754,"domain_scores_codex":[0.9990327,0.00005117106,0.000341639,0.0001701199,0.0001293678,0.0002749478],"domain_scores_gemma":[0.9991148,0.0003354987,0.0002265125,0.00009520482,0.0001250748,0.0001028929],"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":[0.000172099,0.00005870938,0.0002852286,0.000005807935,0.0000669197,0.00007207916,0.0006790789,0.001520824,0.003842251,0.002507724,0.0002423374,0.9905469],"study_design_scores_gemma":[0.0009779052,0.0006754979,0.003710211,0.00009366476,0.00002041915,0.000739207,0.0002044638,0.981292,0.002778567,0.004637646,0.00467041,0.0002000216],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.188467,0.000265354,0.8096198,0.000577562,0.0007544532,0.00005801973,4.29756e-7,0.00001398782,0.0002433716],"genre_scores_gemma":[0.9843211,0.0002606525,0.01347028,0.0002657785,0.001647875,2.853982e-7,3.484147e-7,0.00001079868,0.00002286283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9903469,"threshold_uncertainty_score":0.4384607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02400424574147176,"score_gpt":0.2355081348332628,"score_spread":0.211503889091791,"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."}}