{"id":"W2149715677","doi":"10.1109/test.1989.82379","title":"Design-for-testability using test design yield and decision theory","year":2003,"lang":"en","type":"article","venue":"","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Design for testing; Testability; Computer science; Very-large-scale integration; Test (biology); Reliability engineering; Extension (predicate logic); Set (abstract data type); Test set; Test design; Mathematical optimization; Mathematics; Machine learning; Test method; Statistics; Engineering; Embedded system; Programming language","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":[0.002244855,0.0001193764,0.0001296413,0.00005443156,0.0002467821,0.0001632266,0.0002639747,0.00005332498,0.0000170332],"category_scores_gemma":[0.00342398,0.00009763673,0.00003157901,0.0002300396,0.00004488202,0.0003253046,0.0000535158,0.00006757769,0.000004230812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002568253,"about_ca_system_score_gemma":0.00009694972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000665375,"about_ca_topic_score_gemma":0.000001101737,"domain_scores_codex":[0.9989467,0.000098,0.0001890358,0.0003791299,0.0001234456,0.0002637006],"domain_scores_gemma":[0.9911202,0.008317166,0.00004925086,0.0003374238,0.0000824684,0.00009352653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000002869801,0.000184364,0.01421258,0.00003172011,0.00001335309,0.0000151773,0.0004868234,0.004778327,0.05234789,0.1078942,0.0001964904,0.8198362],"study_design_scores_gemma":[0.0003767372,0.0002421483,0.001171972,0.00007391302,0.00001461,0.0001048059,0.0000657765,0.4756618,0.02782184,0.4940223,0.00005451466,0.0003895026],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01605326,0.0001406638,0.9826649,0.00001668466,0.00007254577,0.000236897,3.017124e-7,0.0001203865,0.0006943747],"genre_scores_gemma":[0.5677487,0.000002149755,0.4321019,0.0001100914,0.00001005415,0.000003681994,4.870789e-8,0.000004758173,0.00001863844],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8194467,"threshold_uncertainty_score":0.4099071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1052049914818865,"score_gpt":0.2799419533405105,"score_spread":0.174736961858624,"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."}}