{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001575789,0.0001079527,0.0001142661,0.00008545741,0.0001073429,0.0000243752,0.00005444163,0.00006595051,0.00002216952],"category_scores_gemma":[0.000006014822,0.00009061621,0.00003024836,0.0001861155,0.0000154447,0.0001271562,0.000002620236,0.00006552829,0.0000387183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000609711,"about_ca_system_score_gemma":0.0000149202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003192999,"about_ca_topic_score_gemma":0.0001318171,"domain_scores_codex":[0.9993944,0.00000546211,0.0001528574,0.0001391937,0.00008514302,0.000222977],"domain_scores_gemma":[0.9995927,0.0001215939,0.00003237365,0.0001108361,0.0000820768,0.0000604601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005215349,0.0003603899,0.002026769,0.0009841786,0.0005944459,0.00001314787,0.001449343,0.077646,0.1922827,0.282681,0.1511013,0.2903392],"study_design_scores_gemma":[0.00462695,0.0007989539,0.001614089,0.0001815423,0.000455429,0.000574602,0.005073642,0.2489603,0.5577191,0.009360378,0.1683068,0.002328291],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001837481,0.00004362908,0.9745299,0.000002581156,0.0001178664,0.0006104654,0.00001788188,0.0003292169,0.02251101],"genre_scores_gemma":[0.995784,0.000006824383,0.001548847,0.00003883408,0.0001270507,0.0002427982,0.0001459341,0.00002814698,0.002077495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9939466,"threshold_uncertainty_score":0.3695222,"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."}}