{"id":"W4298120541","doi":"10.1002/jnm.3068","title":"Nonlinear modeling and digital predistortion for <scp>HF</scp> transmitters with harmonic cancelation","year":2022,"lang":"en","type":"article","venue":"International Journal of Numerical Modelling Electronic Networks Devices and Fields","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services; University of Calgary","funders":"National Key Research and Development Program of China","keywords":"Predistortion; Intermodulation; Harmonics; Amplifier; Nonlinear distortion; Adjacent channel power ratio; Linearization; Electronic engineering; Nonlinear system; Control theory (sociology); Computer science; Total harmonic distortion; Distortion (music); Volterra series; Power (physics); Bandwidth (computing); Engineering; Telecommunications; Physics; Electrical engineering; Artificial intelligence; Voltage","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":[],"consensus_categories":[],"category_scores_codex":[0.0001161713,0.0001154468,0.0001522086,0.00005857959,0.00007808869,0.00006728582,0.0001305944,0.00004464297,0.000002590656],"category_scores_gemma":[0.00000379564,0.0001086658,0.0000508846,0.00006605322,0.00001465765,0.000300188,0.00001505164,0.000424602,4.886091e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001475957,"about_ca_system_score_gemma":0.00003735872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005451257,"about_ca_topic_score_gemma":0.000003171107,"domain_scores_codex":[0.9991494,0.00001054299,0.0002765226,0.0001245044,0.0002235176,0.000215554],"domain_scores_gemma":[0.9995779,0.0001295649,0.0001016022,0.00004233307,0.00008779424,0.00006082196],"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":[0.00005865926,0.00001459698,0.0001939264,0.000008717418,0.0001221345,0.000002883258,0.0001735816,0.9913355,0.00000295588,0.0001124763,0.0000353796,0.007939224],"study_design_scores_gemma":[0.0004512664,0.0003119755,0.000007691042,0.00002307522,0.00003326308,0.0000972484,0.00007078438,0.9940684,0.000003810246,0.001280813,0.003587916,0.0000637713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05203656,0.003462947,0.9439812,0.0001322147,0.0002382586,0.00009234368,0.00000821792,0.00002370863,0.00002455129],"genre_scores_gemma":[0.9942858,0.0006949732,0.00461861,0.00009457565,0.0002389978,0.00001402134,0.00001622256,0.00002451248,0.00001227965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9422492,"threshold_uncertainty_score":0.4431265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008779464497144686,"score_gpt":0.2091489948572407,"score_spread":0.2003695303600961,"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."}}