{"id":"W2059554788","doi":"10.1109/wamicon.2010.5461870","title":"A neural network based on-line adaptive predistorter for power amplifier","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Predistortion; Amplifier; Computer science; Artificial neural network; Control theory (sociology); Electronic engineering; Topology (electrical circuits); Artificial intelligence; Engineering; Electrical engineering; Telecommunications","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":[0.0001031603,0.000226894,0.0001842419,0.00004983352,0.0000575221,0.00002410119,0.000165033,0.0001643585,0.0005039286],"category_scores_gemma":[0.00002819542,0.0002048723,0.00009821175,0.0001099482,0.00003726244,0.0001079742,0.00001243562,0.0004344661,0.00005537961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003134373,"about_ca_system_score_gemma":0.00001456438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001515353,"about_ca_topic_score_gemma":0.00001860659,"domain_scores_codex":[0.9989787,0.000008669284,0.0001974719,0.0002514343,0.0001298424,0.0004338978],"domain_scores_gemma":[0.9991474,0.0002314554,0.00002268405,0.0004116713,0.00005529885,0.000131477],"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":[0.0001711757,0.00004094272,0.0001293541,0.0000131787,0.00003433588,0.000004472889,0.0000616719,0.9196167,0.00165793,0.004399922,0.0715847,0.002285626],"study_design_scores_gemma":[0.0007017389,0.000255987,0.0003556559,0.000009080111,0.00001456226,0.000002714485,0.000008830427,0.9306058,0.0008545167,0.001485909,0.06535472,0.0003504774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002446317,0.00001871621,0.9744984,0.00008796831,0.002332519,0.0005345483,0.00003537941,0.0005890996,0.01945709],"genre_scores_gemma":[0.928906,3.305221e-7,0.06854272,0.0008136767,0.0004509056,0.0001505371,0.00001862309,0.00008720847,0.001030049],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9264596,"threshold_uncertainty_score":0.8354449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002615768713119,"score_gpt":0.2430393751295844,"score_spread":0.2230132174424532,"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."}}