{"id":"W2164564825","doi":"10.1109/ftcs.1991.146656","title":"Multiple fault analysis using a fault dropping technique","year":2002,"lang":"en","type":"article","venue":"","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Benchmark (surveying); Fault (geology); Set (abstract data type); Combinational logic; Stuck-at fault; Computer science; Automatic test pattern generation; Algorithm; Speedup; Electronic circuit; Fault coverage; Parallel computing; Fault detection and isolation; Logic gate; Engineering; Artificial intelligence; Electrical engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002226599,0.000143458,0.0002190824,0.0003689521,0.0002361483,0.000194946,0.000630852,0.00006767899,0.00009220868],"category_scores_gemma":[0.0001229525,0.0001302879,0.0001714042,0.002193379,0.00002533358,0.0004437145,0.0001644361,0.0001378819,0.00004910296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004998039,"about_ca_system_score_gemma":0.00001171269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002611967,"about_ca_topic_score_gemma":0.00003997452,"domain_scores_codex":[0.9986756,0.00004773142,0.0002571852,0.0004295949,0.0002362766,0.0003535806],"domain_scores_gemma":[0.9991065,0.0001067306,0.00008821642,0.0005121312,0.00008549931,0.0001009783],"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":[2.596595e-7,0.0004530646,0.1084505,0.00006385073,0.0008560407,0.0003233543,0.003218293,0.04847509,0.2403906,0.01064752,0.000819695,0.5863017],"study_design_scores_gemma":[0.00008892523,0.00001127375,0.0004153807,0.00001496158,0.00004198292,0.00002831784,0.00003002848,0.9956446,0.003140254,0.0001551174,0.0002273573,0.0002018208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0198483,0.0000635624,0.9716427,0.000104274,0.00004347416,0.0001052896,7.17989e-7,0.0004997379,0.007691988],"genre_scores_gemma":[0.9182504,0.000003398819,0.08109707,0.0002547196,0.00004154054,0.000008412384,8.479936e-7,0.000007278664,0.0003362924],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9471695,"threshold_uncertainty_score":0.5312987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04839260053106336,"score_gpt":0.2594121469517583,"score_spread":0.211019546420695,"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."}}