{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005408288,0.001428944,0.001475924,0.002042668,0.0009372601,0.001563636,0.002702887,0.0013428,0.002031395],"category_scores_gemma":[0.018678,0.001077576,0.001872484,0.00116667,0.001231726,0.004088588,0.002559969,0.002725085,0.0006906004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001651781,"about_ca_system_score_gemma":0.002946387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005046054,"about_ca_topic_score_gemma":0.007499926,"domain_scores_codex":[0.9950886,0.001752066,0.0002854454,0.0006164836,0.002043452,0.0002140042],"domain_scores_gemma":[0.9933003,0.003500008,0.0004763319,0.001501007,0.001094927,0.000127354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001770118,0.000154965,0.00263006,0.0002019353,0.0001665631,0.0001860859,0.0003180939,0.8276039,0.00744072,0.04547715,0.001560735,0.1140828],"study_design_scores_gemma":[0.000007574256,0.00001378662,0.00005216775,0.00000671549,0.00001323027,0.00002957556,0.00001313056,0.9803823,0.001850038,0.01682395,0.0008002409,0.000007231087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003033771,0.00002533793,0.995471,0.00009099244,0.000009276627,0.00003277403,0.00003975995,0.0009791281,0.0003180828],"genre_scores_gemma":[0.2336641,0.00008921511,0.7646214,0.0001561872,0.000026879,0.000211167,0.0004156886,0.0002807365,0.0005346293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005408288,"threshold_uncertainty_score":0.02860212,"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."}}