{"id":"W2141301039","doi":"10.1109/rme.2007.4401798","title":"Time-interleaved incremental data converters with low oversampling ratios","year":2007,"lang":"en","type":"article","venue":"","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Oversampling; Interleaving; Bandwidth (computing); Converters; Computer science; Electronic engineering; Boosting (machine learning); SIGNAL (programming language); Engineering; Artificial intelligence; Electrical engineering; Telecommunications; Voltage","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":[0.0002467527,0.0001342328,0.0001299397,0.00005863818,0.00004194221,0.00003167915,0.0002358041,0.00004355217,0.0005460667],"category_scores_gemma":[0.000006452619,0.0001145906,0.00001952005,0.00008739832,0.00003821613,0.0002476693,0.00002973913,0.0001036634,0.0003008017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005882293,"about_ca_system_score_gemma":0.00001399991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004087035,"about_ca_topic_score_gemma":0.00007511894,"domain_scores_codex":[0.999239,0.000007813989,0.0001623726,0.0001873811,0.0001449038,0.0002585129],"domain_scores_gemma":[0.9994915,0.00005901275,0.00001695319,0.000325312,0.00001745992,0.00008982234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005339033,0.0002759416,0.005583301,0.000421606,0.002128368,0.0006452111,0.002891716,0.0163575,0.7315363,0.01354437,0.1607511,0.06533071],"study_design_scores_gemma":[0.007020505,0.0006837798,0.002001085,0.0004200117,0.0003406515,0.0002612014,0.005850945,0.7199497,0.2430342,0.0003892032,0.01711513,0.002933627],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03084348,0.00003709034,0.9447118,0.000002775205,0.00009018448,0.0001316879,0.00001371814,0.0003205953,0.02384864],"genre_scores_gemma":[0.9984499,0.000003605532,0.0004682491,0.000183927,0.00007281297,0.000001390965,0.0001260686,0.00002894696,0.000665103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9676064,"threshold_uncertainty_score":0.5979047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01650521615336174,"score_gpt":0.2174479666094448,"score_spread":0.2009427504560831,"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."}}