{"id":"W1968787329","doi":"10.1109/tim.2011.2170915","title":"Distributed Spatiotemporal Neural Network for Nonlinear Dynamic Transmitter Modeling and Adaptive Digital Predistortion","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Predistortion; Baseband; Computer science; Amplifier; Electronic engineering; Transmitter; Linearization; Artificial neural network; Doherty amplifier; Adaptive filter; Nonlinear system; Wideband; Algorithm; RF power amplifier; Artificial intelligence; Engineering; CMOS; 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":[0.00008030451,0.000159545,0.000116726,0.00005400059,0.0001226033,0.00003226311,0.00003068228,0.00005101545,0.00000786983],"category_scores_gemma":[9.625433e-7,0.0001701865,0.00004178242,0.00006498133,0.0000286473,0.0003493862,3.459376e-7,0.00009849477,0.000001060031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001299771,"about_ca_system_score_gemma":0.00001071758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008211634,"about_ca_topic_score_gemma":0.0000448706,"domain_scores_codex":[0.9992211,0.00001215174,0.0002334155,0.0001871624,0.0001694454,0.000176739],"domain_scores_gemma":[0.999742,0.000009724258,0.00002824503,0.00007731085,0.0000606881,0.00008204526],"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":[0.0006013226,0.0001367547,0.0001067937,0.0001066482,0.0002064487,0.000001101901,0.001892315,0.7747581,0.001422578,0.00003967279,0.00004986797,0.2206784],"study_design_scores_gemma":[0.001099274,0.0002246247,0.0002717992,0.00003954032,0.00006365514,0.000004334414,0.0002183595,0.9946267,0.00286287,0.0003058671,0.0000755365,0.0002074671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08018417,0.00005065182,0.9184864,0.0000186077,0.0003995038,0.0004866284,0.0002078911,0.0001271752,0.00003897338],"genre_scores_gemma":[0.9878814,0.00003335891,0.01183952,0.00002661132,0.00002083396,0.0001211501,0.0000442664,0.00002666662,0.000006220068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9076972,"threshold_uncertainty_score":0.6940002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04786666618618762,"score_gpt":0.2286015724576078,"score_spread":0.1807349062714202,"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."}}