{"id":"W2108923702","doi":"10.1109/mwsym.2013.6697626","title":"Reconfigurable Doherty amplifier for efficient amplification of signals with variable PAPR","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Predistortion; Amplifier; Adjacent channel power ratio; Doherty amplifier; Electronic engineering; dBc; Power (physics); Crest factor; Computer science; Electrical engineering; Bandwidth (computing); Engineering; RF power amplifier; Telecommunications; CMOS; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000159642,0.0001777685,0.0002356214,0.00007342152,0.00004455136,0.00003510249,0.0001564397,0.00007735934,0.001225871],"category_scores_gemma":[0.00002337232,0.0001399729,0.00003972479,0.000225524,0.00003546832,0.0001617684,0.000005638522,0.00007672514,0.00009938047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004873953,"about_ca_system_score_gemma":0.0000242126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005526757,"about_ca_topic_score_gemma":0.000002183589,"domain_scores_codex":[0.9989747,0.00001280198,0.0003203801,0.0002282899,0.0001357827,0.0003280549],"domain_scores_gemma":[0.9990628,0.0001739885,0.00006345935,0.0003918519,0.0002185518,0.0000893649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003828869,0.00007970227,0.0001073006,0.0002424715,0.0001054279,1.9147e-7,0.0002074511,0.6529374,0.3108221,0.01122627,0.02039039,0.003843072],"study_design_scores_gemma":[0.002166652,0.0004257453,0.001429501,0.0001854542,0.00009762898,0.00001257045,0.0004710513,0.2928206,0.6312136,0.01703143,0.05288548,0.00126026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007538101,0.00007827723,0.9549211,0.00003897832,0.000121332,0.001042966,0.00001696845,0.0002633529,0.03597898],"genre_scores_gemma":[0.8272635,0.000008002214,0.168076,0.00007370847,0.00004095462,0.000622805,0.00002601527,0.00007899354,0.003810013],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8197255,"threshold_uncertainty_score":0.9996871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01377346025389748,"score_gpt":0.2117590347832127,"score_spread":0.1979855745293152,"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."}}