{"id":"W2990095109","doi":"10.1007/978-3-030-35225-7_7","title":"ADLER: Adaptive Sampling for Precise Monitoring","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Profiling (computer programming); Sampling (signal processing); Adaptive sampling; Anomaly detection; Algorithm; Parallel computing; Real-time computing; Data mining; Programming language; Statistics; Mathematics; Monte Carlo method; Telecommunications","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":[0.001233625,0.0005402802,0.0006592114,0.0005485786,0.0003019747,0.0004179553,0.003177881,0.0004154124,0.000005346677],"category_scores_gemma":[0.0001257188,0.0004627605,0.000233353,0.0004126824,0.0003125281,0.0008300741,0.00106801,0.0006452724,0.0000689633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004765512,"about_ca_system_score_gemma":0.0008635867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001360583,"about_ca_topic_score_gemma":0.000008191225,"domain_scores_codex":[0.9959635,0.0000215047,0.0006027404,0.001778259,0.0008670228,0.0007669444],"domain_scores_gemma":[0.9962468,0.001087348,0.0003294258,0.001736712,0.0004470327,0.0001527084],"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":[0.00002262282,0.00002573052,0.0007870595,0.000195211,0.00002057875,0.000007071352,0.001011871,0.05782446,0.00007694361,0.004966714,0.00001453333,0.9350472],"study_design_scores_gemma":[0.001023766,0.0009015222,0.001133936,0.003098237,0.0000265214,0.00006780924,9.940992e-7,0.8420763,0.004494157,0.1396092,0.005515398,0.00205213],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002865847,0.0006634393,0.9897171,0.0001390754,0.006941921,0.001150151,0.000008472604,0.0002305906,0.0008627045],"genre_scores_gemma":[0.1822776,0.00005978647,0.8152705,0.0002004804,0.001475524,0.0000487806,0.000003492099,0.00005382799,0.0006099491],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9329951,"threshold_uncertainty_score":0.9997824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03729705076844908,"score_gpt":0.2771553264780678,"score_spread":0.2398582757096187,"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."}}