{"id":"W2977953926","doi":"10.1007/978-3-030-32079-9_7","title":"Accelerated Learning of Predictive Runtime Monitors for Rare Failure","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Event (particle physics); Inference; Markov chain; Exploit; Rare events; Sample (material); Sampling (signal processing); Construct (python library); Importance sampling; Artificial intelligence; Machine learning; Data mining; Programming language; Statistics; Mathematics; Monte Carlo method","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":[0.00154252,0.0009180261,0.001425006,0.0006634988,0.0003400322,0.0009206772,0.002422187,0.0008968632,0.003528312],"category_scores_gemma":[0.01011996,0.0005229824,0.0005707254,0.0005140003,0.0005670654,0.001417264,0.001511888,0.002225182,0.0006900573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006637448,"about_ca_system_score_gemma":0.001688106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003091549,"about_ca_topic_score_gemma":0.004926743,"domain_scores_codex":[0.9992977,0.0001730325,0.00003222941,0.0002082506,0.0001734499,0.0001151967],"domain_scores_gemma":[0.9946456,0.003725805,0.0003441258,0.0005929245,0.00049677,0.000194822],"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":[0.0008457031,0.0002327852,0.004273807,0.0001563582,0.0000732593,0.0001166828,0.0001046035,0.5572116,0.004717554,0.01072735,0.005488329,0.4160519],"study_design_scores_gemma":[0.000008897588,0.00002654678,0.0001107073,0.000004379088,0.000006134422,0.00001302646,0.000002799587,0.9966933,0.0004641217,0.002549011,0.0001188028,0.000002279335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07222692,0.001068048,0.9195992,0.0003248546,0.0001332767,0.00005991694,0.0001591576,0.004690264,0.001738334],"genre_scores_gemma":[0.8287143,0.0003644086,0.1657942,0.0001548034,0.000200923,0.0001442902,0.0004340013,0.0003026581,0.003890482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003528312,"threshold_uncertainty_score":0.01180345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01997430481071661,"score_gpt":0.2742073740594166,"score_spread":0.2542330692487,"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."}}