{"id":"W4312977212","doi":"10.1109/ojsscs.2022.3218494","title":"A Reconfigurable Power-Efficient Quantized Analog RF Front-End With Smart Calibration","year":2022,"lang":"en","type":"article","venue":"IEEE Open Journal of the Solid-State Circuits Society","topic":"Radio Frequency Integrated Circuit Design","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Power (physics); Analog signal processing; Calibration; Electronic engineering; Electrical engineering; SIGNAL (programming language); Analog front-end; Radio frequency; Dissipation; Engineering; Signal processing; Computer science; Physics; Digital signal processing; CMOS","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002231673,0.0004498529,0.0003896948,0.0003548791,0.0002308157,0.0008534807,0.001478942,0.0005918976,0.004402309],"category_scores_gemma":[0.0004083486,0.0002207541,0.0002051058,0.0003402965,0.0003408977,0.0008833985,0.0006619119,0.000766859,0.00136082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004108499,"about_ca_system_score_gemma":0.0002402749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002709845,"about_ca_topic_score_gemma":0.0005602714,"domain_scores_codex":[0.9995915,0.00003788482,0.00001638981,0.00008529971,0.0002265045,0.00004234329],"domain_scores_gemma":[0.9998021,0.00004728187,0.00003223762,0.00004555521,0.0000573781,0.00001565595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003987116,0.0001812962,0.0007885279,0.0002321892,0.0000468221,0.0004047056,0.0000944249,0.009213086,0.8215116,0.01048805,0.004052481,0.1525882],"study_design_scores_gemma":[0.0001593932,0.001141082,0.001961669,0.0000559927,0.00007297526,0.001468061,0.00005398838,0.2439437,0.710642,0.004249856,0.03612527,0.0001258403],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09773072,0.000665509,0.8812222,0.0004609558,0.0003207747,0.000205899,0.0002409225,0.004818693,0.01433438],"genre_scores_gemma":[0.7936352,0.0001674787,0.1936992,0.0006684346,0.0001034982,0.00007975561,0.0002092059,0.0001416517,0.01129556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004402309,"threshold_uncertainty_score":0.01472718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02070942284306449,"score_gpt":0.238463781398421,"score_spread":0.2177543585553565,"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."}}