{"id":"W1978072367","doi":"10.1007/s10470-014-0266-4","title":"Complexity-reduced Volterra series model for power amplifier digital predistortion","year":2014,"lang":"en","type":"article","venue":"Analog Integrated Circuits and Signal Processing","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predistortion; Volterra series; Behavioral modeling; Amplifier; Computer science; Linearization; Electronic engineering; Wideband; Control theory (sociology); Nonlinear system; Telecommunications; Engineering; Bandwidth (computing); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002757393,0.0006800917,0.0004883124,0.000341675,0.000314704,0.0007622031,0.0008511267,0.0008446838,0.004782443],"category_scores_gemma":[0.00121586,0.0002582616,0.0005198239,0.0003497917,0.0002534699,0.0009833504,0.0003077392,0.001427189,0.001626067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006357211,"about_ca_system_score_gemma":0.0004372283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004234753,"about_ca_topic_score_gemma":0.005434193,"domain_scores_codex":[0.9998293,0.00004113289,0.000007440966,0.00002891566,0.00007578148,0.0000174659],"domain_scores_gemma":[0.9997531,0.0001062076,0.00001570453,0.00003880615,0.0000795323,0.000006728465],"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":[0.00006856213,0.00005168722,0.0002300052,0.0001823775,0.00004739835,0.0001940895,0.0001424959,0.8459725,0.01342874,0.07588566,0.004418694,0.05937767],"study_design_scores_gemma":[0.000001382293,0.000005817852,0.00003207509,0.000004303165,0.000004354213,0.00002389126,0.000003169319,0.9945763,0.0006276354,0.0037673,0.0009506916,0.000002998517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00512107,0.0004385237,0.9858521,0.0001429081,0.00007084699,0.00002227623,0.00008394502,0.0003457105,0.007922571],"genre_scores_gemma":[0.7951859,0.001513768,0.1569007,0.0002826914,0.0001945903,0.0001719202,0.0004765216,0.0003155671,0.04495839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004782443,"threshold_uncertainty_score":0.01599884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02635441338972345,"score_gpt":0.2335314931698353,"score_spread":0.2071770797801118,"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."}}