{"id":"W4401506960","doi":"10.1109/lmwt.2024.3433484","title":"A Novel Digital Predistortion Coefficients Prediction Technique for Dynamic PA Nonlinearities Using Artificial Neural Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Microwave and Wireless Technology Letters","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science Fund for Distinguished Young Scholars; National Science and Technology Major Project","keywords":"Predistortion; Artificial neural network; Computer science; Control theory (sociology); Artificial intelligence; Electronic engineering; Engineering; Telecommunications; Amplifier; Bandwidth (computing)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006433087,0.0002391187,0.0002038133,0.0003611549,0.0001091936,0.0001119303,0.0001146128,0.0002941389,4.568656e-7],"category_scores_gemma":[0.000005997626,0.000265764,0.00006383956,0.0003111647,0.000238448,0.0002558348,0.00002638679,0.0003571716,9.511877e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001573214,"about_ca_system_score_gemma":0.00001037022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001790474,"about_ca_topic_score_gemma":0.00000385209,"domain_scores_codex":[0.9989024,0.000004909815,0.0002890814,0.0003521811,0.00007458751,0.000376886],"domain_scores_gemma":[0.9996884,0.00004445783,0.00003092645,0.0001709471,0.00002853428,0.00003668513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000153373,0.00001793051,0.00003789243,0.0001024512,0.00004456429,0.00001184444,0.00003950278,0.05031148,0.932503,0.0001281924,0.0001552579,0.01663251],"study_design_scores_gemma":[0.000146041,0.00004774059,0.00001152032,0.0001160342,0.00003883674,0.000181391,0.00003789138,0.8927407,0.1058786,0.0002920908,0.0002678348,0.0002413566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3730309,0.0002493848,0.624504,0.000122109,0.0007834349,0.0003800787,0.0001399356,0.0007873534,0.000002784851],"genre_scores_gemma":[0.993629,0.00002052884,0.005847071,0.00005356232,0.0001517708,0.0001389663,0.00007390862,0.00007908979,0.000006121315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8424292,"threshold_uncertainty_score":0.9999794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01109184337796015,"score_gpt":0.2296415709929355,"score_spread":0.2185497276149753,"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."}}