{"id":"W2519152047","doi":"10.1109/wcnc.2016.7564847","title":"Integrating energy harvesting and dynamic spectrum allocation in Cognitive Radio Networks","year":2016,"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":"Carleton University","funders":"","keywords":"Cognitive radio; Computer science; Energy harvesting; Underlay; Energy (signal processing); Context (archaeology); Efficient energy use; Computer network; Spectrum (functional analysis); Radio frequency; Telecommunications; Signal-to-noise ratio (imaging); Wireless; Electrical engineering; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004226003,0.000340975,0.0003127339,0.0002183923,0.0002405894,0.0006224966,0.0005437515,0.0005317907,0.0004183733],"category_scores_gemma":[0.0009389244,0.0001786985,0.0001859304,0.0003012918,0.0004399727,0.0009235677,0.0005764318,0.0003038148,0.0001056867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002466318,"about_ca_system_score_gemma":0.0002731684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006033324,"about_ca_topic_score_gemma":0.0008524757,"domain_scores_codex":[0.9997967,0.00005149054,0.000008884588,0.00004438195,0.00006322975,0.00003517376],"domain_scores_gemma":[0.9997353,0.000166935,0.00002301429,0.00002726004,0.00003532239,0.00001222961],"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":[0.000225596,0.0002162397,0.002064339,0.0001407576,0.00009934277,0.0002224187,0.0001333308,0.5640702,0.07744924,0.0352622,0.0006155291,0.3195007],"study_design_scores_gemma":[0.00001072946,0.0001146482,0.0005115002,0.00000905861,0.00002636066,0.0001791206,0.00003235609,0.9699921,0.01178317,0.01584884,0.001473997,0.00001807338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07470437,0.001626944,0.9185631,0.0001641953,0.00006434049,0.00002561493,0.000006220499,0.0001676076,0.004677508],"genre_scores_gemma":[0.9565124,0.0005227427,0.04167837,0.00005368373,0.0000279765,0.0000152238,0.000005843014,0.00001173019,0.001172034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006224966,"threshold_uncertainty_score":0.002234936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009612920422748712,"score_gpt":0.2273472206558597,"score_spread":0.217734300233111,"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."}}