{"id":"W2029787843","doi":"10.1145/2160803.2160823","title":"Tracking adaptive performance models using dynamic clustering of user classes (abstracts only)","year":2011,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Carleton University; York University","funders":"","keywords":"Cluster analysis; Computer science; Estimator; Data mining; Tracking (education); Class (philosophy); Filter (signal processing); Software; Artificial intelligence; Machine learning; Real-time computing; Computer vision; Mathematics; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005728468,0.0004057394,0.0007599741,0.0006312276,0.0002718375,0.00006066159,0.001867826,0.0001774279,0.0000687135],"category_scores_gemma":[0.0008986693,0.0003500732,0.000214557,0.003229107,0.00009950541,0.004273014,0.0005238486,0.0003626967,0.0000451044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003844746,"about_ca_system_score_gemma":0.0006701329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004197312,"about_ca_topic_score_gemma":0.000007443992,"domain_scores_codex":[0.9953384,0.0002559258,0.001579221,0.0007150398,0.001554685,0.0005567045],"domain_scores_gemma":[0.9950063,0.0002617158,0.001137008,0.001914994,0.001541197,0.0001388254],"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":[0.00003548006,0.0002678008,0.02435603,0.006962276,0.00009833649,0.000002209144,0.001171344,0.05802789,0.0001048581,0.0001846111,0.0000630217,0.9087262],"study_design_scores_gemma":[0.0004069064,0.0002609503,0.06256296,0.004111743,0.0001444596,0.00004501649,0.0000298429,0.9310669,0.0006850433,0.0001433933,0.0001146983,0.0004280536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7872918,0.02353528,0.1850265,0.00004159082,0.0009035741,0.001726894,0.000004204468,0.000178815,0.001291343],"genre_scores_gemma":[0.896797,0.03454795,0.06833207,0.000162371,0.00003620701,0.00006719445,0.000007415891,0.00002791691,0.00002183886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9082981,"threshold_uncertainty_score":0.9998952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1916283713348196,"score_gpt":0.3420866543612551,"score_spread":0.1504582830264355,"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."}}