{"id":"W2104162027","doi":"10.1109/iccad.1993.580118","title":"Cellular Automata Synthesis Based On Precomputed Test Vectors For Built-in Self-test","year":2005,"lang":"en","type":"article","venue":"Proceedings of 1993 International Conference on Computer Aided Design (ICCAD)","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Automatic test pattern generation; Cellular automaton; Test vector; Computer science; Fault coverage; Set (abstract data type); Built-in self-test; Test set; Automaton; Test (biology); Algorithm; Generator (circuit theory); Parallel computing; Simple (philosophy); Code coverage; Theoretical computer science; Embedded system; Programming language; Software; Artificial intelligence; Engineering; Electronic circuit","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":[0.0009280956,0.0004565363,0.0005042555,0.0007937707,0.000130063,0.0004501691,0.002547384,0.0001584292,0.00002745751],"category_scores_gemma":[0.0008107591,0.0004611533,0.000188529,0.0005118045,0.00005493828,0.0007862289,0.0002208465,0.0003153702,0.00004130374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002511285,"about_ca_system_score_gemma":0.0002151998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001842136,"about_ca_topic_score_gemma":0.000002148,"domain_scores_codex":[0.9967772,0.00003033284,0.0008294477,0.00102422,0.0007896074,0.0005492081],"domain_scores_gemma":[0.9954553,0.002689739,0.0005204365,0.000309689,0.0008514757,0.0001733388],"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":[0.0001362405,0.009352154,0.02810252,0.000742009,0.000379416,0.00005699638,0.001688815,0.02984403,0.09851663,0.2266339,0.01259029,0.591957],"study_design_scores_gemma":[0.001029251,0.0007969879,0.002014907,0.0006982588,0.00001548919,0.00000956523,0.000008467086,0.9418794,0.05100809,0.001821826,0.0002723708,0.0004454164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02130508,0.00001278609,0.9686803,0.003299181,0.0005455815,0.0009443642,0.00003141425,0.0007651101,0.004416235],"genre_scores_gemma":[0.8171012,0.000006257319,0.1818981,0.0004534488,0.0003407187,0.0001161498,0.000008094818,0.00003215164,0.00004384971],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9120353,"threshold_uncertainty_score":0.999784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04833540597834448,"score_gpt":0.2653379253201593,"score_spread":0.2170025193418148,"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."}}