{"id":"W4396686657","doi":"10.1145/3629526.3645033","title":"An Adaptive Logging System (ALS): Enhancing Software Logging with Reinforcement Learning Techniques","year":2024,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ciena (Canada); Brock University","funders":"","keywords":"Logging; Computer science; Python (programming language); Software; Source code; Consistency (knowledge bases); Context (archaeology); Software engineering; Operating system; Artificial intelligence","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":[0.003770008,0.0008499633,0.0004796528,0.0007088012,0.0003306678,0.0008191293,0.001753235,0.0006879269,0.0009905957],"category_scores_gemma":[0.01616568,0.000358292,0.0003274372,0.0003298696,0.000775005,0.001532593,0.001334898,0.001301976,0.0004177669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006148052,"about_ca_system_score_gemma":0.001536486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003150774,"about_ca_topic_score_gemma":0.003684251,"domain_scores_codex":[0.9984282,0.0006156174,0.0001000243,0.000335792,0.0004120655,0.0001083221],"domain_scores_gemma":[0.9915845,0.004465378,0.00104933,0.001116932,0.001409635,0.0003742589],"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":[0.0005484083,0.001422453,0.02481508,0.0003111132,0.0001180201,0.0002428193,0.000783278,0.3301562,0.03658716,0.003155302,0.003856841,0.5980032],"study_design_scores_gemma":[0.00004758806,0.0003375589,0.001863155,0.00002272889,0.00002627409,0.00006233822,0.00004126007,0.9843265,0.00970583,0.002063722,0.001469958,0.00003318363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1337251,0.0001431505,0.8472528,0.0002992146,0.00006725113,0.0003192213,0.00008442627,0.01659836,0.001510468],"genre_scores_gemma":[0.6607127,0.00008310096,0.3367018,0.000188617,0.00002946414,0.000279052,0.0001414107,0.0003603309,0.001503506],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003770008,"threshold_uncertainty_score":0.01993793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009185844274080828,"score_gpt":0.2420127382112232,"score_spread":0.2328268939371423,"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."}}