{"id":"W2017031653","doi":"10.1109/iscas.2013.6572415","title":"A low power all-digital self-calibrated temperature sensor using 65nm FPGAs","year":2013,"lang":"en","type":"article","venue":"","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Field-programmable gate array; Overhead (engineering); Calibration; Power (physics); Electronic engineering; Logic gate; Process (computing); Low-power electronics; Embedded system; Real-time computing; Power consumption; Engineering; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002114996,0.0004177526,0.0002928048,0.000412294,0.0001802264,0.000460232,0.0007467798,0.0002222807,0.001498285],"category_scores_gemma":[0.0003571058,0.0002048883,0.0001326616,0.0002345465,0.0001466325,0.0005757057,0.0002363273,0.0002437787,0.0002971259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003800077,"about_ca_system_score_gemma":0.0003010598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000780521,"about_ca_topic_score_gemma":0.001417562,"domain_scores_codex":[0.9996775,0.00004584422,0.00001739065,0.00008524005,0.000142222,0.00003181605],"domain_scores_gemma":[0.9997551,0.00005058342,0.00006365262,0.00004657502,0.00006510829,0.0000190891],"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":[0.0004486652,0.0001056272,0.005790682,0.0003242583,0.00007562018,0.0002417412,0.0001849869,0.005410664,0.8447717,0.001498704,0.001579568,0.1395678],"study_design_scores_gemma":[0.00008087399,0.001220971,0.008781414,0.000043554,0.0001005887,0.001097604,0.00006398202,0.06566012,0.9054229,0.0002495905,0.01722406,0.00005438282],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6783704,0.001263258,0.3035312,0.0002492401,0.0002401646,0.0002202897,0.0004292722,0.006584402,0.009111709],"genre_scores_gemma":[0.9166368,0.0001819209,0.07927885,0.00009701048,0.00002589107,0.00004470688,0.0001236765,0.00006219201,0.003548887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001498285,"threshold_uncertainty_score":0.005012274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008000148816696032,"score_gpt":0.1833516946518045,"score_spread":0.1753515458351085,"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."}}