{"id":"W4381486903","doi":"10.1016/j.aeue.2023.154787","title":"Wideband RF rectifier circuit for low-powered IoT wireless sensor nodes","year":2023,"lang":"en","type":"article","venue":"AEU - International Journal of Electronics and Communications","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université Laval","funders":"","keywords":"Rectifier (neural networks); Stub (electronics); Impedance matching; Electrical engineering; Wideband; Radio frequency; Electrical impedance; Broadband; Input impedance; Diode; Transmission line; Linearity; Electronic engineering; Engineering; Computer science; Telecommunications","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":[0.0001276263,0.0001602057,0.0002448794,0.0001181862,0.0001464788,0.0003460142,0.0006734559,0.0003691278,0.004031501],"category_scores_gemma":[0.0002358788,0.000109662,0.0002158597,0.0002248852,0.0001085057,0.0005912836,0.0002289614,0.0002804018,0.001650144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001847122,"about_ca_system_score_gemma":0.0001229425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009573581,"about_ca_topic_score_gemma":0.0002167376,"domain_scores_codex":[0.9998873,0.0000137012,0.000009873426,0.00002939637,0.00004408115,0.00001553563],"domain_scores_gemma":[0.9998922,0.00002122076,0.00002086546,0.00001956196,0.00004031369,0.000005799502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002041374,0.00004728412,0.0004787332,0.0004609912,0.00003729365,0.0001573452,0.0001029838,0.001713853,0.8956237,0.006729247,0.004035741,0.09040866],"study_design_scores_gemma":[0.00008491295,0.001410398,0.004163982,0.0001529699,0.0001960583,0.00252273,0.0001727565,0.0625118,0.8205752,0.004063346,0.1040999,0.00004603386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2319676,0.005609096,0.6946837,0.001056874,0.0007877462,0.0002014436,0.0004509131,0.002396993,0.06284568],"genre_scores_gemma":[0.9386504,0.001071549,0.03320447,0.0003213723,0.0001307561,0.0000508984,0.0001860569,0.00009680363,0.02628771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004031501,"threshold_uncertainty_score":0.01348674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02271267835415913,"score_gpt":0.2677041778764467,"score_spread":0.2449914995222876,"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."}}