{"id":"W2124130510","doi":"10.1109/mwsym.2012.6259707","title":"Wiener G-functionals for nonlinear power amplifier digital predistortion","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predistortion; Volterra series; Amplifier; Series (stratigraphy); Pruning; Nonlinear system; dBc; Computer science; Adjacent channel power ratio; Power series; Power (physics); Control theory (sociology); Electronic engineering; Algorithm; Mathematics; Telecommunications; Engineering; Bandwidth (computing); Mathematical analysis; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007464871,0.0001384523,0.000108393,0.00004967731,0.00003933274,0.00003249722,0.00006600432,0.00006966913,0.0003574658],"category_scores_gemma":[0.00003791795,0.0001290291,0.0000702578,0.00007662788,0.00001954899,0.0008396379,0.00001231783,0.0000640998,0.0002271935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007193267,"about_ca_system_score_gemma":0.000006904047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.466039e-7,"about_ca_topic_score_gemma":2.856785e-7,"domain_scores_codex":[0.9992359,0.000003006114,0.0001763327,0.000121106,0.0001243129,0.0003393976],"domain_scores_gemma":[0.9995586,0.00007025426,0.00001759637,0.00018139,0.00004953184,0.0001226084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003696812,0.001022048,0.02345205,0.0003500633,0.0008268647,0.00000310255,0.00213896,0.07142322,0.03212946,0.06818318,0.7458327,0.05426873],"study_design_scores_gemma":[0.0004070662,0.00004718743,0.001823372,0.000007521004,0.00001691261,0.000009870003,0.0000553288,0.004732387,0.004213833,0.001208287,0.9870931,0.0003851367],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007900883,0.0002068944,0.9601635,0.00002018441,0.001503698,0.0002594857,0.00009302561,0.0005019232,0.02935042],"genre_scores_gemma":[0.9716566,0.000003575052,0.02194099,0.0001024324,0.0005355775,0.00009922269,0.0001210054,0.00006855861,0.005472094],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9637557,"threshold_uncertainty_score":0.5261655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508865013613305,"score_gpt":0.2316371830065357,"score_spread":0.2165485328704026,"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."}}