{"id":"W7091663984","doi":"10.1109/tmtt.2025.3616337","title":"Exploring Dynamic Sparse Memory Effect Under Varying Transmission States for Digital Predistortion of RF Power Amplifiers","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services; University of Calgary","funders":"Natural Science Foundation of Ningbo; National Natural Science Foundation of China","keywords":"Predistortion; Amplifier; Transmission (telecommunications); Linearization; Compensation (psychology); Dynamic random-access memory; Representation (politics); Wideband; Power (physics)","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":[0.0002894436,0.0002361132,0.0002533214,0.0002772707,0.0001169547,0.00002776082,0.00009161155,0.00009920575,0.000009718452],"category_scores_gemma":[0.000004407301,0.0002342235,0.0001224178,0.0001563396,0.0001063863,0.0004003351,0.000001195611,0.0001995848,7.420594e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001009205,"about_ca_system_score_gemma":0.00001300875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001244682,"about_ca_topic_score_gemma":7.125354e-7,"domain_scores_codex":[0.999148,0.00004819213,0.0002777848,0.0002407927,0.00007190756,0.0002133228],"domain_scores_gemma":[0.999193,0.0004832335,0.00003535219,0.0002036654,0.00003378255,0.00005096118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006475092,0.00005405598,8.593249e-7,0.000376314,0.000123208,0.000001027741,0.0004736679,0.01108256,0.8020524,0.0008554714,0.00002311794,0.1843098],"study_design_scores_gemma":[0.0003333397,0.0002148467,0.000002656755,0.0003225961,0.00007524582,0.000004215869,0.0001431674,0.0003627387,0.9740583,0.0240188,0.0002580394,0.0002060914],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09506562,0.0002360241,0.9026127,0.000008229316,0.0001973088,0.0006080719,0.00006738304,0.0005558522,0.0006487765],"genre_scores_gemma":[0.9946452,0.0003213156,0.004543415,0.00001855281,0.000005194785,0.000243645,0.00001059885,0.00004781699,0.0001642171],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8995796,"threshold_uncertainty_score":0.9551359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01517285122003273,"score_gpt":0.246778859173964,"score_spread":0.2316060079539313,"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."}}