{"id":"W4321448317","doi":"10.14778/3574245.3574246","title":"Cache Me If You Can","year":2022,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Differential privacy; Cache; Workload; Private information retrieval; CPU cache; Process (computing); Differential (mechanical device); Query optimization; Information retrieval; Data mining; Computer network; Computer security; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001135411,0.0009990138,0.0007815121,0.001128215,0.001818591,0.004327862,0.001521653,0.002375716,0.3334294],"category_scores_gemma":[0.01285906,0.0004831439,0.0006106418,0.001286084,0.0007890896,0.008368731,0.004211869,0.00210075,0.2130059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005991283,"about_ca_system_score_gemma":0.001095539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003478852,"about_ca_topic_score_gemma":0.004960708,"domain_scores_codex":[0.9989876,0.000193127,0.00005047219,0.0001783024,0.0003609678,0.0002294541],"domain_scores_gemma":[0.9959497,0.0007934025,0.0002906288,0.001105451,0.001082797,0.0007778972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003234462,0.00007210387,0.001817262,0.0001709762,0.00002496231,0.000365609,0.0006372207,0.0002397942,0.001120846,0.00999298,0.8370804,0.1481545],"study_design_scores_gemma":[0.00002376293,0.0000389013,0.0005166357,0.00009094088,0.00001722336,0.000520905,0.0004611509,0.0006772364,0.0009094761,0.008229038,0.988475,0.00003969179],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02405338,0.009611816,0.07235532,0.09607065,0.009461508,0.0007370734,0.01770272,0.03485075,0.7351568],"genre_scores_gemma":[0.1221756,0.005264511,0.02409417,0.0416625,0.003263162,0.0004950238,0.009733134,0.007598426,0.7857134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3334294,"threshold_uncertainty_score":0.9507821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02377398276897345,"score_gpt":0.2396947130465157,"score_spread":0.2159207302775422,"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."}}