{"id":"W1932346019","doi":"10.1109/dftvs.1993.595745","title":"Some results on yield and local design rule relaxation","year":2002,"lang":"en","type":"article","venue":"","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Cluster analysis; Exponent; Relaxation (psychology); Computer science; Optimal design; Yield (engineering); Mathematical optimization; Line (geometry); Algorithm; Mathematics; Artificial intelligence; Machine learning; Geometry","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":[0.003637776,0.001937312,0.002050662,0.001343924,0.001177274,0.001963306,0.001644417,0.001408401,0.01042545],"category_scores_gemma":[0.01400708,0.0009184487,0.002199487,0.00205336,0.002095039,0.002464765,0.001564599,0.003438256,0.001536795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001487694,"about_ca_system_score_gemma":0.0008549711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001008668,"about_ca_topic_score_gemma":0.0009572351,"domain_scores_codex":[0.9978011,0.0005179014,0.0000902706,0.000387458,0.0008036913,0.0003995078],"domain_scores_gemma":[0.9910822,0.006593384,0.0007731502,0.0007303189,0.00061648,0.0002043996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003320299,0.0003840334,0.0009121768,0.000803316,0.0002264067,0.0005213859,0.0004716933,0.5668793,0.01451149,0.2859676,0.00912997,0.1198607],"study_design_scores_gemma":[0.00008247706,0.0003580824,0.0009982033,0.0001565495,0.0002009275,0.0003376914,0.0001871034,0.6012782,0.009106128,0.3760557,0.01116542,0.00007330109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04513672,0.004942378,0.8809004,0.001095956,0.0001370034,0.00008791336,0.0002167164,0.0005077223,0.06697515],"genre_scores_gemma":[0.7845218,0.008348359,0.1788403,0.001483909,0.0007556981,0.0003334112,0.0006168002,0.001115182,0.02398456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01042545,"threshold_uncertainty_score":0.03487659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06322303901327955,"score_gpt":0.2288302460989957,"score_spread":0.1656072070857161,"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."}}