{"id":"W2101140468","doi":"10.1109/icmmt.2008.4540693","title":"Accurate modeling of wideband RF Doherty power amplifiers using dynamic nonlinear models","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Amplifier; LDMOS; Nonlinear system; Wideband; Behavioral modeling; Polynomial; Computer science; Doherty amplifier; Power (physics); Electronic engineering; Polynomial and rational function modeling; RF power amplifier; Control theory (sociology); Engineering; Mathematics; Electrical engineering; Artificial intelligence; Voltage; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009267708,0.0002763734,0.0003486074,0.0001443819,0.00008321745,0.00001316319,0.0002158435,0.0001259071,0.00005911948],"category_scores_gemma":[0.00001471745,0.0002668995,0.0001041428,0.0002583556,0.00006561274,0.0005607148,0.00003979803,0.0002109953,0.00001386493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001163139,"about_ca_system_score_gemma":0.00004570613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005172932,"about_ca_topic_score_gemma":0.000008056384,"domain_scores_codex":[0.9985979,0.00001445522,0.0004643878,0.0002705137,0.0002321457,0.0004206381],"domain_scores_gemma":[0.9992469,0.00005001038,0.00004984097,0.000428152,0.0001016226,0.0001234537],"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":[0.00001520562,0.00001495286,0.00001765799,0.00002422577,0.00004241037,0.000009729424,0.0003246416,0.9796213,0.01965117,0.0001204624,0.00007787755,0.0000803677],"study_design_scores_gemma":[0.000326549,0.00001603124,0.000003649536,0.0000307154,0.00001458333,0.00003836372,0.00009576231,0.9962839,0.0016717,0.001154644,0.00004729716,0.0003167338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1953585,0.0002768761,0.7992778,0.000004593635,0.0001815157,0.0001556675,0.00001427284,0.0003934545,0.004337326],"genre_scores_gemma":[0.916624,0.0001178583,0.08292501,0.00003788333,0.00001656192,0.000004412137,0.00000704097,0.000086915,0.000180299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7212655,"threshold_uncertainty_score":0.9999783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0445390402830633,"score_gpt":0.2590584397303725,"score_spread":0.2145193994473092,"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."}}