{"id":"W2129684888","doi":"10.1145/1458082.1458197","title":"Records retention in relational database systems","year":2008,"lang":"en","type":"article","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Data retention; Records management; Scope (computer science); Legislation; Computer science; Identification (biology); Database; Relational database; Business; Information retrieval; Knowledge management; Computer security; Law; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0254952,0.0003972822,0.00107641,0.003290087,0.002273597,0.00724005,0.005002973,0.00186016,0.003683535],"category_scores_gemma":[0.08489539,0.00124407,0.000951678,0.007858446,0.002475774,0.018732,0.005770389,0.002956972,0.002430578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002171947,"about_ca_system_score_gemma":0.003530044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004704408,"about_ca_topic_score_gemma":0.002110756,"domain_scores_codex":[0.9706916,0.007600082,0.004061061,0.002335344,0.01354935,0.001762639],"domain_scores_gemma":[0.9427776,0.02486121,0.006305241,0.0158685,0.009115388,0.001071949],"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":[0.0007596428,0.0004032515,0.02191108,0.001392672,0.0001411673,0.0008626824,0.004147513,0.02242218,0.004737584,0.2802223,0.02077396,0.642226],"study_design_scores_gemma":[0.0003640761,0.001200501,0.007408237,0.001435566,0.0005227803,0.003664327,0.003429797,0.304304,0.03851354,0.4058306,0.2329477,0.0003789076],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09788015,0.0136434,0.8492395,0.007337613,0.000493139,0.0008313955,0.001490556,0.007998778,0.02108545],"genre_scores_gemma":[0.6778435,0.007360545,0.2899057,0.001561379,0.0008603346,0.00048256,0.00286885,0.0007619007,0.01835527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0254952,"threshold_uncertainty_score":0.1348331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4765472888744886,"score_gpt":0.4172024478028538,"score_spread":0.05934484107163485,"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."}}