{"id":"W4394676698","doi":"10.1109/access.2024.3387287","title":"Landscape and Taxonomy of Online Parser-Supported Log Anomaly Detection Methods","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"JST-Mirai Program; Japan Society for the Promotion of Science; Japan Science and Technology Corporation; Japan Society for the Promotion of Science London; Polytechnique Montréal","keywords":"Computer science; Anomaly detection; Parsing; Workflow; Data mining; USable; Anomaly (physics); Taxonomy (biology); Artificial intelligence; Database; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004962328,0.0001012249,0.0001809136,0.0001273161,0.00004525956,0.0001564127,0.0004220289,0.00007675719,0.00001365409],"category_scores_gemma":[0.0000236183,0.00007693464,0.00005053999,0.0004916529,0.0000398398,0.0008497555,0.0001223418,0.0001076601,0.000006449512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001391649,"about_ca_system_score_gemma":0.00006879013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000850781,"about_ca_topic_score_gemma":0.00003341588,"domain_scores_codex":[0.9990799,0.00007867466,0.0002625611,0.0003204305,0.0001132434,0.0001451525],"domain_scores_gemma":[0.999315,0.0001453456,0.00006573084,0.000355277,0.00006745857,0.00005114658],"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":[0.00001393042,0.00007863055,0.08934745,0.000597887,0.00007115566,0.00001682569,0.0002757631,0.0001200966,0.005279718,0.0001072966,0.0005420034,0.9035493],"study_design_scores_gemma":[0.0007361055,0.0003766255,0.2853139,0.0003015773,0.00008300383,0.0001866351,0.00007497017,0.4454384,0.2271743,0.002155352,0.03749102,0.0006681157],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4915427,0.0004375713,0.5064836,0.0000751375,0.001002878,0.0001417376,0.000003658016,0.0001498018,0.000162944],"genre_scores_gemma":[0.9800816,0.00004335499,0.01964358,0.000042054,0.0001094974,0.00002757369,0.000001765639,0.000005900198,0.00004472895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9028811,"threshold_uncertainty_score":0.3137303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04720225800036703,"score_gpt":0.3494964284430565,"score_spread":0.3022941704426894,"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."}}