{"id":"W4246422893","doi":"10.1109/iccad.2015.7372655","title":"Reducing post-silicon coverage monitoring overhead with emulation and Bayesian feature selection","year":2015,"lang":"en","type":"article","venue":"2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Observability; Computer science; Emulation; Metric (unit); Overhead (engineering); Bayesian probability; Set (abstract data type); Data mining; Coverage probability; Algorithm; Artificial intelligence; Engineering; Statistics; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006144272,0.0003999438,0.0003284554,0.0003708045,0.0002334419,0.0009033838,0.00105424,0.0001747648,0.00001061475],"category_scores_gemma":[0.0001801727,0.0003723209,0.00005938891,0.0003679587,0.0000563772,0.001367225,0.00019348,0.0004852571,0.00003547808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003175072,"about_ca_system_score_gemma":0.000395648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001635543,"about_ca_topic_score_gemma":0.00001152967,"domain_scores_codex":[0.9971809,0.0002318679,0.0003823986,0.0009026122,0.0008704195,0.0004317846],"domain_scores_gemma":[0.9975008,0.0002925288,0.0003216141,0.0005172362,0.001046445,0.0003213682],"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":[0.0002594076,0.0005441129,0.02775815,0.0001005959,0.0004897622,0.000318367,0.005791129,0.1141678,0.06065261,0.07048613,0.007203146,0.7122288],"study_design_scores_gemma":[0.001500993,0.001246498,0.009231597,0.0005424276,0.00002064104,0.0003326556,0.00007254216,0.9715769,0.006830728,0.007818923,0.0001609868,0.0006650547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07401767,0.00004618391,0.920801,0.001803643,0.001535905,0.0003207866,0.000005806061,0.0003410099,0.001128027],"genre_scores_gemma":[0.9359673,0.00001625893,0.06259429,0.0002576093,0.0008264013,0.00001926773,0.00001743351,0.00002875228,0.0002726977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8619496,"threshold_uncertainty_score":0.9998729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08073843140237887,"score_gpt":0.3001636183520596,"score_spread":0.2194251869496807,"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."}}