{"id":"W2184253632","doi":"10.1109/ssd.2015.7348122","title":"Optimized node classification and channel pairing scheme for RF energy harvesting based cognitive radio sensor networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Cognitive radio; Computer science; Computer network; Efficient energy use; Wireless sensor network; Node (physics); Energy harvesting; Wireless; Key distribution in wireless sensor networks; Spectral efficiency; Channel (broadcasting); Energy (signal processing); Wireless network; Telecommunications; Engineering; Electrical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006045407,0.0002224528,0.0002749892,0.0001152662,0.0002373062,0.0003432402,0.000172042,0.0001009796,0.00000184537],"category_scores_gemma":[0.0002545879,0.0002130138,0.0000745216,0.0003126385,0.00006910682,0.0004250845,0.0000905872,0.0001230369,0.000001328364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006490031,"about_ca_system_score_gemma":0.00008538098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000633932,"about_ca_topic_score_gemma":0.00002330734,"domain_scores_codex":[0.9983854,0.00009844748,0.0002765387,0.0005920409,0.0001864807,0.0004611023],"domain_scores_gemma":[0.9982761,0.0007524865,0.0001437553,0.0002197327,0.0003480762,0.0002598906],"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":[0.001303308,0.0005428317,0.003255521,0.0001277039,0.0005020515,0.0001544307,0.002061046,0.2862629,0.003178103,0.08230525,0.00483381,0.615473],"study_design_scores_gemma":[0.002226874,0.00008216466,0.0005052877,0.00009558013,0.00001880053,0.00002496716,0.0001579301,0.9953914,0.0005938045,0.0003513418,0.0002496733,0.0003021932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003582201,0.0002634673,0.9924086,0.001052111,0.0002442467,0.0002556518,0.000002171076,0.0002703911,0.001921208],"genre_scores_gemma":[0.7265503,0.0000172274,0.2719881,0.0007968138,0.000327891,0.00003002972,0.00001883651,0.00002309017,0.0002477458],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7229681,"threshold_uncertainty_score":0.868645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05234017316454109,"score_gpt":0.2561297730923681,"score_spread":0.203789599927827,"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."}}