{"id":"W4389636146","doi":"10.1016/j.engappai.2023.107649","title":"Microservices performance forecast using dynamic Multiple Predictor Systems","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco","keywords":"Computer science; Autoregressive integrated moving average; Robustness (evolution); Pooling; Random forest; Performance prediction; Support vector machine; Data mining; Boosting (machine learning); Time series; Machine learning; Artificial intelligence; Simulation","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":[0.0001485524,0.0001328847,0.0001336268,0.0003019406,0.00005939656,0.00003173857,0.0002596017,0.00006252685,0.000003311206],"category_scores_gemma":[0.00001042498,0.0001547781,0.0000402623,0.000776541,0.00002924573,0.0001287358,0.00004238407,0.0001003448,0.00005447036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005605426,"about_ca_system_score_gemma":0.000007783084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001734283,"about_ca_topic_score_gemma":0.000005520679,"domain_scores_codex":[0.9991169,0.000003827616,0.0003579211,0.0001584624,0.0001287409,0.0002341352],"domain_scores_gemma":[0.9995286,0.00004444648,0.00004066776,0.0002839323,0.00004949171,0.00005281893],"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":[0.000001671405,0.00001304065,0.00004802784,0.0002912583,0.00002070472,2.571206e-7,0.00009627389,0.9580715,0.02471708,0.001562308,0.00008987287,0.01508797],"study_design_scores_gemma":[0.00001085044,0.0000109031,0.0002447877,0.00005958992,0.00001058752,0.000001648928,0.0001353605,0.977559,0.01994853,0.00003262439,0.001857925,0.0001281391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2544741,0.0001166097,0.7405505,0.000006123212,0.0002479628,0.0003724382,0.00003015649,0.004131217,0.00007095211],"genre_scores_gemma":[0.9942141,0.0001690179,0.005228776,0.000001672018,0.00005621662,0.0002517934,0.00002709634,0.00003557104,0.00001582597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.73974,"threshold_uncertainty_score":0.6311667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02066341503822661,"score_gpt":0.243905932089341,"score_spread":0.2232425170511144,"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."}}