{"id":"W1976485031","doi":"10.1049/el.2009.0584","title":"Excess loop delay compensation for continuous-time ΔΣ modulators using interpolation","year":2009,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Differentiator; Control theory (sociology); Feed forward; Compensation (psychology); Interpolation (computer graphics); Transfer function; Band-pass filter; SIGNAL (programming language); Computer science; Loop (graph theory); Feedback loop; Filter (signal processing); Low-pass filter; Mathematics; Electronic engineering; Engineering; Telecommunications; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003095129,0.0003137289,0.0002456574,0.0003345375,0.0002499495,0.0004053579,0.0006400519,0.000428114,0.001470456],"category_scores_gemma":[0.0005414143,0.0001629092,0.0001673692,0.0004153708,0.0002344915,0.0007313769,0.0003237009,0.0005835281,0.000314444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003749781,"about_ca_system_score_gemma":0.0003072207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002489604,"about_ca_topic_score_gemma":0.000641738,"domain_scores_codex":[0.9997626,0.00002827311,0.00001532387,0.00003931724,0.0001360721,0.00001828688],"domain_scores_gemma":[0.9997203,0.00008528581,0.00006623254,0.00004244064,0.00007074192,0.00001493292],"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":[0.0003480981,0.00004523413,0.0006075519,0.0001933815,0.00002164402,0.0001317734,0.0001033217,0.004309509,0.8143209,0.008659885,0.0005397642,0.170719],"study_design_scores_gemma":[0.00009864249,0.0008495177,0.001332317,0.00006578116,0.0000654801,0.001196312,0.00003107122,0.1267791,0.8425842,0.002688498,0.02424623,0.00006286384],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1035503,0.001927339,0.8892878,0.0001686825,0.0001825446,0.00005223581,0.00003892457,0.0009138084,0.003878367],"genre_scores_gemma":[0.6232647,0.001072653,0.37045,0.0001323981,0.0001228782,0.00004516126,0.00007357401,0.00005511842,0.004783557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001470456,"threshold_uncertainty_score":0.004919112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009212645431088955,"score_gpt":0.2133741225114512,"score_spread":0.2041614770803622,"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."}}