{"id":"W2418598077","doi":"10.1145/2908557","title":"Write Skew and Zipf Distribution","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Storage","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Zipf's law; Computer science; Skew; Workload; Benchmark (surveying); Block (permutation group theory); Class (philosophy); Variety (cybernetics); Parallel computing; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009877777,0.0001372421,0.0001189694,0.00009191086,0.0001961716,0.00005293458,0.0007606256,0.00008305671,0.00002182132],"category_scores_gemma":[0.00008181501,0.0001004549,0.00003767958,0.0002870713,0.0001314715,0.000945597,0.00004670416,0.0001348132,0.00009779324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009940597,"about_ca_system_score_gemma":0.00001776483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006353173,"about_ca_topic_score_gemma":0.000007149531,"domain_scores_codex":[0.9990373,0.0000266088,0.0001374432,0.000402027,0.0001668997,0.0002296826],"domain_scores_gemma":[0.9984092,0.000186047,0.00004705958,0.001256287,0.00003523551,0.00006621284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009838845,0.00006352244,0.00001907534,0.000005791644,0.00001322962,0.00002297243,0.00005112758,0.00002013357,0.003859916,0.02134228,0.0004456442,0.9741465],"study_design_scores_gemma":[0.005640647,0.001620176,0.01122907,0.0004317242,0.00009423251,0.0004697603,0.0002213556,0.002676843,0.1563363,0.3208208,0.4977325,0.002726616],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008243836,0.00009594767,0.9866135,0.003723158,0.000212521,0.0001044735,0.0001600827,0.0007820768,0.00006448037],"genre_scores_gemma":[0.9345832,0.0002105366,0.06469554,0.0001040622,0.00001450264,0.00003012308,0.000004746073,0.000009269182,0.000348048],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9714199,"threshold_uncertainty_score":0.4096433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01648730979935262,"score_gpt":0.2471854480837103,"score_spread":0.2306981382843577,"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."}}