{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00100437,0.001402159,0.0007754657,0.0007507031,0.0003815606,0.0009013144,0.001987087,0.001162227,0.002909301],"category_scores_gemma":[0.004416271,0.0007286546,0.0007570678,0.000948581,0.0007618513,0.002353453,0.001282501,0.00327987,0.0007440367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001823804,"about_ca_system_score_gemma":0.001924716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01620896,"about_ca_topic_score_gemma":0.01905674,"domain_scores_codex":[0.9995354,0.00008824225,0.00002824372,0.0001359506,0.0001354062,0.00007679337],"domain_scores_gemma":[0.9988981,0.0004820263,0.0001082981,0.0002545213,0.0002100497,0.00004694052],"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":[0.0001063186,0.00009071723,0.001613167,0.000134317,0.00008332497,0.00007327236,0.00006035675,0.8192979,0.00224167,0.006525415,0.005081017,0.1646925],"study_design_scores_gemma":[0.000002459151,0.000009893994,0.00008672246,0.000005658634,0.000003695956,0.000006366653,0.000004518897,0.9938306,0.0008752279,0.004685648,0.0004862201,0.00000294985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0350811,0.001439692,0.9520642,0.0008568118,0.0001106986,0.00005634932,0.0008670249,0.007325734,0.002198376],"genre_scores_gemma":[0.7931458,0.0009894953,0.1955503,0.0005539898,0.0000855056,0.0001761543,0.003036122,0.0004346982,0.006027841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01620896,"threshold_uncertainty_score":0.03222919,"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."}}