{"id":"W4400409408","doi":"10.1109/lmwt.2024.3420398","title":"A Residual Selectable Modeling Method Based on Deep Neural Network for Power Amplifiers With Multiple States","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":"","keywords":"Residual; Artificial neural network; Amplifier; Power (physics); Computer science; Deep neural networks; Artificial intelligence; Electronic engineering; Engineering; Telecommunications; Algorithm; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000383931,0.0007636626,0.0003666249,0.0002930667,0.0002453758,0.0003947837,0.001089359,0.0004403632,0.001631936],"category_scores_gemma":[0.0005248034,0.0003542857,0.0008262435,0.0002530647,0.000300394,0.0009837759,0.0004406611,0.0009984757,0.0002991474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005739882,"about_ca_system_score_gemma":0.0006675483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008038083,"about_ca_topic_score_gemma":0.01234354,"domain_scores_codex":[0.9998092,0.00004115835,0.00001312136,0.0000566603,0.00006162794,0.00001829642],"domain_scores_gemma":[0.999874,0.00004088482,0.00002043246,0.00002026661,0.00003769763,0.000006736226],"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":[0.00007896575,0.00004018916,0.0007126071,0.00009905185,0.00008059604,0.0001066257,0.00009433907,0.8232829,0.02520406,0.01617423,0.001159494,0.132967],"study_design_scores_gemma":[0.000001323196,0.00001075602,0.00003155196,0.000001822496,0.000006845014,0.000009364835,0.000001818324,0.9973568,0.001284881,0.0009019892,0.0003899049,0.000002949529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004798365,0.0001358107,0.993582,0.00004962101,0.00001608622,0.00001153795,0.00002774471,0.0004025981,0.0009762957],"genre_scores_gemma":[0.6981606,0.0005850614,0.292867,0.0001579398,0.00003951207,0.0001563212,0.0002689333,0.0001948928,0.007569803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008038083,"threshold_uncertainty_score":0.01598257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007991170187271994,"score_gpt":0.2257444933313108,"score_spread":0.2177533231440388,"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."}}