{"id":"W2738612849","doi":"10.1109/tcad.2017.2729458","title":"Bit-Flip Detection-Driven Selection of Trace Signals","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"CMC Microsystems","keywords":"Debugging; TRACE (psycholinguistics); Computer science; Selection (genetic algorithm); Software; Focus (optics); Identification (biology); Bit (key); Extension (predicate logic); Chip; Embedded system; Computer engineering; Programming language; Artificial intelligence; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000667809,0.0003185589,0.0006200185,0.0004414995,0.0006751398,0.0003880379,0.0008425274,0.0002020308,0.000006781101],"category_scores_gemma":[0.00002821429,0.0002858754,0.0001644843,0.0004242979,0.0001411823,0.0006616072,0.000005092973,0.0003485097,0.000006378721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007306215,"about_ca_system_score_gemma":0.0001617847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007244727,"about_ca_topic_score_gemma":0.00002916388,"domain_scores_codex":[0.9975653,0.000320624,0.0008012489,0.0005836973,0.0003953883,0.0003336925],"domain_scores_gemma":[0.9974388,0.0004166294,0.0007313085,0.0007013873,0.0005607851,0.0001511309],"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":[0.00000678688,0.0002730932,0.0001335121,0.0001459423,0.0002230188,0.00001287543,0.0005046756,0.1791737,0.2532955,0.0005315462,0.00004400452,0.5656554],"study_design_scores_gemma":[0.0005730609,0.0008444482,0.0004078177,0.0005423033,0.00004164648,0.0001385751,0.00006053098,0.8731689,0.1238165,0.00009187564,0.00002900902,0.0002853269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03507836,0.0001033064,0.9628291,0.00002694698,0.001196947,0.0004469791,0.00001738436,0.0001697124,0.0001312508],"genre_scores_gemma":[0.9978855,0.00003936256,0.001839135,0.00001603022,0.00008096108,0.00003218839,6.496275e-7,0.00002266553,0.00008351282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9628071,"threshold_uncertainty_score":0.9999593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04722724355126078,"score_gpt":0.2527988955484167,"score_spread":0.2055716519971559,"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."}}