{"id":"W1944137603","doi":"10.1109/iscas.1993.394147","title":"A logic-enhanced memory for digital data recovery circuits","year":2002,"lang":"en","type":"article","venue":"1993 IEEE International Symposium on Circuits and Systems","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Digital data; Digital electronics; Semiconductor memory; Pixel; Computer hardware; Logic gate; Range (aeronautics); Electronic circuit; Artificial intelligence; Algorithm; Data transmission; Electrical engineering; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002760394,0.000266121,0.0003080658,0.0001661673,0.0001597473,0.0007969839,0.002762155,0.0001384291,0.000008768133],"category_scores_gemma":[0.0002365317,0.0002417512,0.00006326944,0.0001793577,0.00008269417,0.00233793,0.0004064804,0.0001646476,0.00009302456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001413507,"about_ca_system_score_gemma":0.00001712544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001694875,"about_ca_topic_score_gemma":0.000003872417,"domain_scores_codex":[0.9975988,0.00002791016,0.0004667416,0.001017731,0.0005299632,0.0003588065],"domain_scores_gemma":[0.9976864,0.0003876361,0.000273752,0.001389873,0.0001672407,0.000095057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003048703,0.0007255861,0.0001540979,0.000266359,0.0005300384,0.0001555572,0.0008554083,0.005023759,0.04598462,0.1571641,0.05826094,0.730849],"study_design_scores_gemma":[0.005488603,0.00204458,0.0002679126,0.001061243,0.00006331722,0.0009784591,0.0006052745,0.7117047,0.009808208,0.01815831,0.2465255,0.003293982],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007027931,0.0006551341,0.9622734,0.001833174,0.00654903,0.0008646292,0.001512789,0.0007104347,0.01857345],"genre_scores_gemma":[0.9960109,0.0002079793,0.0004625249,0.0002832228,0.0004131393,0.0001209201,0.0001444423,0.00002422567,0.002332624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.988983,"threshold_uncertainty_score":0.9858326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07417229178957581,"score_gpt":0.2825442226492954,"score_spread":0.2083719308597196,"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."}}