{"id":"W2111280711","doi":"10.1109/iscas.2007.378860","title":"A Nanowatt Successive Approximation ADC with Offset Correction for Implantable Sensor Applications","year":2007,"lang":"en","type":"article","venue":"","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Comparator; Offset (computer science); CMOS; Successive approximation ADC; Electrical engineering; Voltage; Electronic engineering; Computer science; 12-bit; Analog-to-digital converter; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004606928,0.0003400973,0.0003323314,0.0004555568,0.0003056338,0.000610469,0.001114764,0.0006316496,0.006977636],"category_scores_gemma":[0.0008833871,0.0002423049,0.0001677501,0.0005372095,0.0001425461,0.000915633,0.0002854725,0.0006755756,0.002838016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002906792,"about_ca_system_score_gemma":0.0004315182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002661686,"about_ca_topic_score_gemma":0.0009234449,"domain_scores_codex":[0.9995364,0.00004134182,0.00003062586,0.00006728345,0.0002934211,0.00003093225],"domain_scores_gemma":[0.9995595,0.0001052836,0.00004208219,0.00006409539,0.0001902225,0.00003894833],"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":[0.000270765,0.00009093178,0.0008047164,0.0002972266,0.00002660081,0.0002404365,0.00007781146,0.001657307,0.699879,0.006153735,0.005636568,0.2848649],"study_design_scores_gemma":[0.00009213942,0.001401089,0.002843511,0.00006662515,0.0001244359,0.005486842,0.00006315443,0.09036627,0.7732946,0.002232842,0.1239138,0.0001146736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05746692,0.001559616,0.9118478,0.0006418682,0.0005460478,0.0002424302,0.0004526339,0.00776775,0.01947492],"genre_scores_gemma":[0.3366627,0.0009967397,0.6371879,0.0004827601,0.0001770072,0.0001414473,0.0005122475,0.0001968397,0.02364235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006977636,"threshold_uncertainty_score":0.02334255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007543759959193332,"score_gpt":0.2116169017067532,"score_spread":0.2040731417475599,"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."}}