{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003285542,0.0003268057,0.0002926122,0.0002837332,0.0001437089,0.0008948126,0.0008263513,0.0006401114,0.003801551],"category_scores_gemma":[0.0005431949,0.0001499659,0.0002423802,0.0001948384,0.0002776707,0.0006938649,0.0006349561,0.0004362976,0.0017404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004043313,"about_ca_system_score_gemma":0.0001942204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000170741,"about_ca_topic_score_gemma":0.0001895583,"domain_scores_codex":[0.9995834,0.00003282498,0.00002441503,0.00005489631,0.0002644761,0.00003986623],"domain_scores_gemma":[0.9997076,0.00004672906,0.00004772311,0.00005979885,0.0001166202,0.00002146762],"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":[0.0003409588,0.0001280134,0.001251604,0.0004197685,0.00004601807,0.0003205551,0.0002963604,0.007830145,0.7949786,0.04269756,0.004851026,0.1468393],"study_design_scores_gemma":[0.0000796832,0.001020198,0.002202793,0.00006437846,0.00004849218,0.0006769017,0.00005907599,0.05179534,0.8452196,0.005821495,0.09295624,0.00005571311],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2800253,0.002265139,0.6520601,0.00060065,0.0005848276,0.0003421934,0.00068121,0.006181846,0.05725879],"genre_scores_gemma":[0.8321732,0.000640768,0.1344605,0.0004287352,0.00008986016,0.0001531358,0.000402819,0.0001362152,0.03151478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003801551,"threshold_uncertainty_score":0.01271749,"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."}}