{"id":"W2793317393","doi":"10.1007/s10472-018-9578-x","title":"Efficient suspect selection in unreachable state diagnosis","year":2018,"lang":"en","type":"article","venue":"Annals of Mathematics and Artificial Intelligence","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Benchmark (surveying); Suspect; Computer science; Set (abstract data type); Root cause; Root (linguistics); Root cause analysis; Speedup; Selection (genetic algorithm); State (computer science); Process (computing); Algorithm; Mathematical optimization; Reliability engineering; Machine learning; Mathematics; Parallel computing; Programming language; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002407063,0.0009791021,0.001822256,0.002285051,0.0008662963,0.002028891,0.002204791,0.001419062,0.004410488],"category_scores_gemma":[0.01842549,0.0006076643,0.0008172644,0.001079882,0.001434398,0.002535673,0.003045565,0.001594018,0.0007243655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000977647,"about_ca_system_score_gemma":0.002661938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003066452,"about_ca_topic_score_gemma":0.005077098,"domain_scores_codex":[0.9967346,0.001069646,0.0002129758,0.0004872122,0.001115898,0.0003796713],"domain_scores_gemma":[0.9798629,0.01650745,0.0006343932,0.001544224,0.001097508,0.0003535623],"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":[0.004497287,0.0005570778,0.009337553,0.0006316933,0.0001767423,0.001531242,0.000568077,0.2681378,0.01915941,0.06389286,0.0113579,0.6201524],"study_design_scores_gemma":[0.0001124824,0.0001372468,0.0004138047,0.00002158251,0.00004950451,0.0002254698,0.00007778027,0.9305256,0.008450883,0.05906498,0.000904793,0.00001597087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1127544,0.0005429235,0.8783686,0.001157171,0.0001119706,0.0002218927,0.0004069894,0.003708166,0.002727892],"genre_scores_gemma":[0.7987322,0.0001150595,0.1972999,0.0002363459,0.00006816259,0.0000859258,0.0006578325,0.0001944718,0.002610141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004410488,"threshold_uncertainty_score":0.01475453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1215684213925238,"score_gpt":0.3374677578527973,"score_spread":0.2158993364602735,"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."}}