{"id":"W2939483835","doi":"10.1109/ijcnn.2019.8852116","title":"Deep Learning for System Trace Restoration","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; TRACE (psycholinguistics); Lossy compression; Benchmark (surveying); Data mining; Noise (video); Deep learning; Sequence (biology); Event (particle physics); Data cleansing; Field (mathematics); Anomaly detection; Data quality; Artificial intelligence; Algorithm; Machine learning; Image (mathematics)","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":[0.000895824,0.0001975982,0.0003255668,0.00009013481,0.0001371269,0.0002505555,0.0008421352,0.0003459215,0.000002895793],"category_scores_gemma":[0.00007143572,0.000158993,0.0001702012,0.0001034507,0.0000127461,0.00027401,0.0004769602,0.0003675306,0.0001610194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002276061,"about_ca_system_score_gemma":0.000161394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005214243,"about_ca_topic_score_gemma":0.000007936639,"domain_scores_codex":[0.9983726,0.0001011292,0.0003831817,0.0006478637,0.0002629931,0.0002322935],"domain_scores_gemma":[0.9982975,0.0001649166,0.0002556455,0.0009897233,0.0002405467,0.00005168752],"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":[0.00004935755,0.0001019916,0.01662162,0.01596513,0.0001410112,0.000004607184,0.004551656,0.794238,0.00008281492,0.04575493,0.003772112,0.1187167],"study_design_scores_gemma":[0.0001854388,0.00007141767,0.001215378,0.000235755,0.000009595646,0.000005720281,0.0001256313,0.9919506,0.0001262344,0.0002449643,0.005576625,0.0002526363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008429773,0.000343297,0.9830008,0.0001687658,0.003714818,0.00111868,7.259127e-7,0.0008543415,0.002368779],"genre_scores_gemma":[0.9535822,0.00001151718,0.04398243,0.0000220511,0.000248903,0.0002034916,0.00001582936,0.0000149387,0.001918642],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9451524,"threshold_uncertainty_score":0.6483548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01881858938212279,"score_gpt":0.2585692955336533,"score_spread":0.2397507061515305,"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."}}