{"id":"W1493361109","doi":"10.1109/bigdatacongress.2015.112","title":"A Flexible Data-Driven Approach for Execution Trace Filtering","year":2015,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; TRACE (psycholinguistics); XML; Kernel (algebra); Abstraction; Event (particle physics); Executable; Simple (philosophy); Range (aeronautics); Data mining; Programming language; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006267671,0.00008203266,0.0001182782,0.0000373525,0.00006860525,0.00009582622,0.001012102,0.00004995818,0.000002213713],"category_scores_gemma":[0.00005230113,0.00006138076,0.00003044468,0.0001670812,0.00001833423,0.000987128,0.0003422938,0.0000456805,0.00002659099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004024769,"about_ca_system_score_gemma":0.00008462367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003759664,"about_ca_topic_score_gemma":0.000001751537,"domain_scores_codex":[0.9990568,0.00002187795,0.0001657188,0.000388643,0.0001716353,0.0001953205],"domain_scores_gemma":[0.9987006,0.00003180663,0.00004163236,0.001047492,0.00008689417,0.00009155225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002395739,0.00147968,0.01155357,0.001642823,0.0002018641,0.000008243966,0.01225223,0.07694092,0.001854409,0.06348813,0.4204017,0.4099368],"study_design_scores_gemma":[0.0003931611,0.00007617233,0.0002212945,0.000007479598,0.00000355571,0.00001347428,0.00009665567,0.9789873,0.0008794525,0.0004071934,0.01878529,0.000128946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003786708,0.00005946461,0.9910246,0.0001651723,0.0003360908,0.0003219058,0.000005773335,0.0003853223,0.003915025],"genre_scores_gemma":[0.3867096,0.000002271608,0.6121379,0.00007049294,0.0001262363,0.00005807285,0.00003457623,0.000005751469,0.0008550344],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9020464,"threshold_uncertainty_score":0.2503035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1333221482199372,"score_gpt":0.3158756389875,"score_spread":0.1825534907675629,"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."}}