{"id":"W3160315930","doi":"10.1049/cds2.12076","title":"Characterizing a standard cell library for large scale design of memristive based signal processing","year":2021,"lang":"en","type":"article","venue":"IET Circuits Devices & Systems","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Memristor; Computer science; Adder; CMOS; Electronic engineering; Computer architecture; Subtractor; Resistive random-access memory; Standard cell; Computer engineering; Computer hardware; Integrated circuit; Electrical engineering; Engineering; Voltage","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000242032,0.000218694,0.0004346671,0.0000695404,0.0001334808,0.0001047075,0.0001582112,0.00008950038,0.0000169766],"category_scores_gemma":[0.00001014513,0.0002275357,0.00008973957,0.0002908723,0.00001379651,0.0004908469,0.00002582845,0.0001455027,0.000003271695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003815204,"about_ca_system_score_gemma":0.0001263097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.727376e-7,"about_ca_topic_score_gemma":6.745241e-7,"domain_scores_codex":[0.9985796,0.00008878382,0.0004491789,0.0002971661,0.0001987056,0.000386518],"domain_scores_gemma":[0.999178,0.0002701121,0.0001743055,0.0001623246,0.0001253418,0.00008992178],"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":[0.00008073096,0.0000939482,0.0008802814,0.01315198,0.0000722781,0.00008017888,0.002238554,0.1717344,0.7967695,0.00005085594,0.0002899966,0.0145573],"study_design_scores_gemma":[0.0008180419,0.00009499623,0.0001000135,0.001345682,0.00004706053,0.00001620356,0.000975399,0.3282288,0.6606035,0.00001851817,0.007382208,0.0003696645],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09954033,0.004692044,0.8930312,0.00001628722,0.0004606365,0.0006795975,0.0002694823,0.000460242,0.0008502323],"genre_scores_gemma":[0.9972346,0.00001064319,0.002179309,0.00006738338,0.0002441761,0.00005512563,0.00005545172,0.00007720305,0.00007607132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8976943,"threshold_uncertainty_score":0.9278638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02140709281817908,"score_gpt":0.2325132050692894,"score_spread":0.2111061122511103,"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."}}