{"id":"W7125642748","doi":"10.1109/cascon66301.2025.00033","title":"Framework for SLA-Breach Prediction in Cloud-Native Microservices","year":2025,"lang":"","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Microservices; Software; Field (mathematics); Set (abstract data type); Context (archaeology)","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":[0.001458553,0.0003513148,0.0005110411,0.0003432347,0.0003485781,0.0003105933,0.001166981,0.0005774189,0.00007208333],"category_scores_gemma":[0.0002382459,0.0003046972,0.000214549,0.001858098,0.0001330558,0.0008133741,0.0004133385,0.0004806785,0.00006331003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00031432,"about_ca_system_score_gemma":0.0004586957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004075201,"about_ca_topic_score_gemma":0.0001647112,"domain_scores_codex":[0.9968278,0.0001702082,0.0009813145,0.001073902,0.0002967608,0.0006499502],"domain_scores_gemma":[0.9973605,0.0009175535,0.0002083418,0.001012112,0.000411323,0.00009015525],"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":[0.0005124253,0.001637245,0.4067364,0.005219602,0.0003425004,0.000005514609,0.02401971,0.001739495,0.0002268132,0.2859377,0.0119736,0.261649],"study_design_scores_gemma":[0.003401866,0.0007563519,0.1752148,0.004002461,0.00009023847,0.000007505514,0.002116725,0.5569233,0.006502944,0.2240409,0.02598169,0.0009612435],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08985335,0.001195849,0.8912818,0.00300273,0.009311069,0.001769642,0.00003692054,0.0002112013,0.003337472],"genre_scores_gemma":[0.9508221,0.000114198,0.04403685,0.001094182,0.0004101397,0.0002916862,0.000009196138,0.00001302395,0.003208666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8609687,"threshold_uncertainty_score":0.9999405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01511703289682755,"score_gpt":0.3001686577411257,"score_spread":0.2850516248442981,"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."}}