{"id":"W2915016908","doi":"10.14778/3291264.3291274","title":"Shrinkwrap","year":2018,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Differential privacy; SQL; Padding; Set (abstract data type); Cardinality (data modeling); Query optimization; Operator (biology); Sargable; Database; Web search query; Information retrieval; Data mining; Computer security; Search engine","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":[0.0001653164,0.00008681665,0.00009195426,0.00005664871,0.0001266196,0.00007293649,0.001443589,0.00002651697,0.00001985387],"category_scores_gemma":[0.00003018258,0.00005683336,0.00008775057,0.0004523067,0.0001370946,0.0003487649,0.0007186309,0.00007202637,0.00002033605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001338801,"about_ca_system_score_gemma":0.00001303417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002123769,"about_ca_topic_score_gemma":0.00000227375,"domain_scores_codex":[0.9991575,0.00000283008,0.0001521376,0.0002224984,0.0002771954,0.0001878665],"domain_scores_gemma":[0.9994425,0.00001261854,0.0001105151,0.0002366637,0.0001493888,0.00004831002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007355646,0.00008117831,0.002610633,0.00002144933,0.00001791259,1.047762e-7,0.001255245,8.587849e-8,0.01658216,0.965081,0.009414755,0.004928171],"study_design_scores_gemma":[0.0005931985,0.0003012681,0.01172111,0.00008362894,0.00001914872,0.0000206516,0.0002080869,0.000725446,0.7085136,0.2011248,0.07641447,0.0002745895],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7545118,0.0007315383,0.02248862,0.008864042,0.003023513,0.001333294,0.00002749166,0.00062808,0.2083916],"genre_scores_gemma":[0.984395,0.00001389726,0.0151702,0.000271928,0.0001061364,0.00001110032,1.727437e-7,0.000003540384,0.00002800481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7639562,"threshold_uncertainty_score":0.2682571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00835529035135328,"score_gpt":0.2125817958908677,"score_spread":0.2042265055395145,"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."}}