{"id":"W4386245102","doi":"10.32920/24050745.v1","title":"Self Sustainable Cognitive Wireless Sensor Networks with RF Energy Harvesting","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Energy harvesting; Wireless sensor network; Wireless; Computer science; Energy (signal processing); Computer network; Radio frequency; Wireless network; Power (physics); Telecommunications; Physics","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":[0.000274617,0.0003668082,0.0003635183,0.0001996981,0.0003458546,0.0006608926,0.0009126011,0.0004255117,0.0007288348],"category_scores_gemma":[0.0003697755,0.0001802539,0.000453357,0.0003906452,0.0003665845,0.0007990036,0.0006174822,0.0004361857,0.0001995426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000378748,"about_ca_system_score_gemma":0.0004277713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001490373,"about_ca_topic_score_gemma":0.00187538,"domain_scores_codex":[0.9998247,0.00003886723,0.000007000723,0.00003957202,0.00005922352,0.00003059298],"domain_scores_gemma":[0.9998786,0.00004590411,0.00001995609,0.00001673388,0.00003062382,0.000008144897],"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":[0.0001750935,0.0001101791,0.001010414,0.0003037741,0.0001059778,0.0003623211,0.0002497629,0.7729345,0.03674771,0.0651773,0.002851838,0.1199711],"study_design_scores_gemma":[0.000006443447,0.00006283513,0.0001543943,0.000007503841,0.00001247459,0.00005615641,0.00002972591,0.989073,0.001433837,0.007651714,0.001503583,0.000008283276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08967397,0.002449378,0.8853264,0.0004497885,0.0001998797,0.0000663068,0.00005881677,0.0003289675,0.0214465],"genre_scores_gemma":[0.9590651,0.001564301,0.03299441,0.0001322601,0.000062,0.00007941091,0.00003297254,0.00001400793,0.006055621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001490373,"threshold_uncertainty_score":0.002963364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.011812546518899,"score_gpt":0.2058807229815084,"score_spread":0.1940681764626093,"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."}}