{"id":"W2019193046","doi":"10.1109/mtv.2012.23","title":"Progressive-BackSpace: Efficient Predecessor Computation for Post-Silicon Debug","year":2012,"lang":"en","type":"article","venue":"","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Debugging; Observability; TRACE (psycholinguistics); State (computer science); Set (abstract data type); Overhead (engineering); Computer engineering; Embedded system; Computation; Chip; Instruction set; Software bug; Finite-state machine; Image (mathematics); System on a chip; Parallel computing; Algorithm; Software; Programming language; Artificial intelligence","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":[0.000885426,0.001300653,0.0006774866,0.001826428,0.0004545145,0.0009141807,0.002393561,0.0007928115,0.007290416],"category_scores_gemma":[0.00346761,0.0006028208,0.0007023418,0.0007953675,0.0008854037,0.002380465,0.001923009,0.0007829906,0.00109109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007532476,"about_ca_system_score_gemma":0.001637906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002858773,"about_ca_topic_score_gemma":0.005095879,"domain_scores_codex":[0.9993514,0.0001684654,0.00005069356,0.0001091532,0.0002322766,0.00008783939],"domain_scores_gemma":[0.9980718,0.0009218777,0.0001596691,0.000569285,0.0002109968,0.00006641859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001593127,0.0003144556,0.005401009,0.0004031465,0.00009367345,0.0003274299,0.0004196232,0.1503365,0.03745353,0.02778526,0.008901337,0.766971],"study_design_scores_gemma":[0.0001776149,0.0003680514,0.0004687281,0.00003219012,0.00002962485,0.0001716691,0.00005348185,0.9310827,0.04281227,0.01927117,0.00548792,0.00004462145],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03846765,0.0003150868,0.9403354,0.0000998318,0.00004687607,0.0001458866,0.0001945288,0.01879177,0.001602923],"genre_scores_gemma":[0.374842,0.0001326491,0.6209418,0.0001049031,0.00002297153,0.0001777685,0.0004941237,0.0005907178,0.002693162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007290416,"threshold_uncertainty_score":0.02438891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02354208874445178,"score_gpt":0.2887115280504051,"score_spread":0.2651694393059533,"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."}}