{"id":"W2152384991","doi":"10.1109/icac.2008.33","title":"Semantic-Driven Model Composition for Accurate Anomaly Diagnosis","year":2008,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Bayesian network; Component (thermodynamics); Data mining; Artificial intelligence; Machine learning; Hierarchy; Benchmark (surveying); Set (abstract data type); Anomaly detection; Sketch; Key (lock); Algorithm","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":[0.0001237318,0.0001061777,0.000155395,0.0000512832,0.0002553939,0.0000419718,0.0004150741,0.00005998544,0.000006414375],"category_scores_gemma":[0.00001230074,0.00008227152,0.00009662179,0.0001599976,0.00003680151,0.0006128868,0.00009170873,0.0000455714,0.00007252306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003327915,"about_ca_system_score_gemma":0.00005744553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002776412,"about_ca_topic_score_gemma":0.000004782058,"domain_scores_codex":[0.9990981,0.0000191126,0.0002150903,0.0002986119,0.0001512312,0.0002178425],"domain_scores_gemma":[0.9992309,0.0001044849,0.00005773618,0.000417976,0.0001250468,0.00006388276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001220204,0.00161859,0.6000956,0.0007580828,0.0002037814,0.00005014432,0.007171271,0.2166922,0.003802581,0.05057199,0.09073431,0.0281794],"study_design_scores_gemma":[0.0003199897,0.00007064839,0.009489825,0.00001499761,0.000004345732,0.00002204089,0.000004145482,0.9854053,0.00360076,0.000612727,0.0003069387,0.0001482404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3277581,0.00001823263,0.670704,0.0005005164,0.0001508444,0.0002620964,0.00000302078,0.0002131435,0.0003900288],"genre_scores_gemma":[0.9111596,0.00003941298,0.08809302,0.0003055405,0.00004474938,0.0001473144,0.000004349474,0.000005612498,0.0002003846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7687131,"threshold_uncertainty_score":0.3354935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0367809286409323,"score_gpt":0.2648871459968284,"score_spread":0.2281062173558961,"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."}}