{"id":"W17444570","doi":"10.1002/smll.200600727","title":"Characterizing Computer Systems' Workloads","year":2002,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Workload; Computer science; Process (computing); Set (abstract data type); Characterization (materials science); Distributed computing; Operating system","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":[0.0009075424,0.0006083423,0.0004001924,0.003214639,0.0003799754,0.001283744,0.0005808126,0.0003029607,0.005348753],"category_scores_gemma":[0.009817514,0.0001789882,0.000194308,0.003221233,0.0001869351,0.002184462,0.0006766539,0.0004035498,0.001942749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006055096,"about_ca_system_score_gemma":0.0004793808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002138056,"about_ca_topic_score_gemma":0.003304261,"domain_scores_codex":[0.9979233,0.0002851654,0.00018822,0.0003571841,0.0009814773,0.0002647234],"domain_scores_gemma":[0.9949181,0.001953943,0.0006463441,0.0005144654,0.00149972,0.0004674318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007321417,0.0004142965,0.5151367,0.0009619847,0.0003585899,0.0005274279,0.001149344,0.04348189,0.02164643,0.01488376,0.05237123,0.3483362],"study_design_scores_gemma":[0.00006080517,0.0005870698,0.6163052,0.0001505571,0.0001456683,0.001674137,0.002220195,0.2360409,0.02091411,0.02038655,0.1014048,0.0001101222],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8937266,0.003201362,0.02119046,0.001137154,0.0001960178,0.0004281349,0.03523631,0.0023399,0.04254412],"genre_scores_gemma":[0.9740173,0.0008298955,0.006300511,0.0001268895,0.0001039457,0.0001465969,0.01482815,0.0001196565,0.003527104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005348753,"threshold_uncertainty_score":0.01789331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01903712165096911,"score_gpt":0.2080489229522376,"score_spread":0.1890118013012685,"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."}}