{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001236979,0.0005193767,0.0006735258,0.0004593048,0.0008351034,0.0004844887,0.001120987,0.0004121894,0.0007403871],"category_scores_gemma":[0.002423217,0.0002237617,0.0002750926,0.0006098493,0.0005191878,0.0008648619,0.001012948,0.0005059417,0.0002439089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004410269,"about_ca_system_score_gemma":0.001000441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007103064,"about_ca_topic_score_gemma":0.0009573564,"domain_scores_codex":[0.9989207,0.000412255,0.00005666136,0.0001773217,0.000260563,0.0001725412],"domain_scores_gemma":[0.9991297,0.0002638061,0.0001573202,0.0001776059,0.0001922986,0.00007921317],"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":[0.001309233,0.000631159,0.005520137,0.000155264,0.00008864618,0.0003121062,0.000568818,0.5783024,0.070067,0.03485132,0.003415875,0.304778],"study_design_scores_gemma":[0.00003595613,0.0002251362,0.0006368031,0.000004965385,0.00001950324,0.000150132,0.00005419934,0.9846831,0.008752398,0.004649485,0.0007659531,0.00002242962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09365808,0.0001705873,0.9038233,0.00009429821,0.0000775142,0.0001329465,0.00003333784,0.0003001877,0.001709843],"genre_scores_gemma":[0.9159828,0.00008898037,0.08281972,0.00004665727,0.00002511471,0.0001072442,0.00006678516,0.0000118309,0.0008508046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001236979,"threshold_uncertainty_score":0.006541848,"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."}}