{"id":"W4291414035","doi":"10.3390/chips1020008","title":"Integrated Sensor Electronic Front-Ends with Self-X Capabilities","year":2022,"lang":"en","type":"article","venue":"Chips","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Food Inspection Agency","keywords":"Computer science; Interfacing; Robustness (evolution); System on a chip; Integrated circuit; Computer architecture; Application-specific integrated circuit; CMOS; Embedded system; Neuromorphic engineering; Electronic engineering; Computer hardware; Engineering; Artificial intelligence; Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006558842,0.0001138694,0.00011057,0.00003864115,0.0001679617,0.000009381898,0.00009462076,0.00001478196,0.0001672728],"category_scores_gemma":[0.000006250864,0.0001005901,0.000025653,0.0001221612,0.00001404527,0.00005829359,0.00002880733,0.0004283135,0.00001155274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001609166,"about_ca_system_score_gemma":0.00002190933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002667426,"about_ca_topic_score_gemma":0.000009944723,"domain_scores_codex":[0.9993721,0.00003253503,0.000091357,0.0001314201,0.0000889592,0.0002836084],"domain_scores_gemma":[0.9997626,0.00004291959,0.00001390849,0.0001356823,0.00001053499,0.00003435712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002002373,0.0001142197,0.001819675,0.0002545851,0.0002535857,0.0001183416,0.008536622,0.9170853,0.05383474,0.002838152,0.002128136,0.0128164],"study_design_scores_gemma":[0.003826403,0.002253155,0.002134425,0.0001010089,0.0001596737,0.001089983,0.01977102,0.249962,0.2319835,0.004871835,0.4808676,0.002979413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949304,0.0003283554,0.001267864,0.00002726027,0.0001557786,0.00008620903,0.000006162011,0.0007759237,0.002422006],"genre_scores_gemma":[0.9986209,0.000007928764,0.0006350381,0.00004566071,0.00005906323,0.00002279343,0.00001126961,0.00002981452,0.0005675186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6671233,"threshold_uncertainty_score":0.4101947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007209879915644142,"score_gpt":0.1827896790870317,"score_spread":0.1755797991713876,"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."}}