{"id":"W2168592511","doi":"10.1109/icde.2009.39","title":"PSALM: Cardinality Estimation in the Presence of Fine-Grained Access Controls","year":2009,"lang":"en","type":"article","venue":"Proceedings - International Conference on Data Engineering","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Tuple; Cardinality (data modeling); Computer science; Exploit; Access control; Process (computing); Data access; Database; Information retrieval; Data mining; Theoretical computer science; Computer security; Mathematics; Programming language","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":[0.0005506838,0.0001307656,0.0001649641,0.0001324362,0.0000299338,0.0001955771,0.00244753,0.00003002278,0.000006032572],"category_scores_gemma":[0.0006655439,0.0001020957,0.00002292427,0.0002614881,0.00002008237,0.00264524,0.0003138072,0.0001578345,0.000002145252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002879791,"about_ca_system_score_gemma":0.0000317016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003975879,"about_ca_topic_score_gemma":0.000004216304,"domain_scores_codex":[0.9987546,0.000008848311,0.0003073213,0.0003405341,0.0004293125,0.0001593404],"domain_scores_gemma":[0.999123,0.00009227498,0.0001446088,0.0004136484,0.0001956993,0.0000307921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001530706,0.00004406835,0.0002414745,0.00002901658,0.00001219433,0.000003555968,0.0002927692,0.003656334,0.002066595,0.9863281,0.0002561567,0.007054488],"study_design_scores_gemma":[0.0002580539,0.00004970868,0.005268841,0.0001876538,0.000003051762,0.0000147694,0.0000507171,0.9891661,0.0007233713,0.002716629,0.001432453,0.0001286867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01511923,0.00003548529,0.9778453,0.00311054,0.0002758804,0.0003869538,0.0001559552,0.00009401456,0.002976651],"genre_scores_gemma":[0.9731305,0.0000143585,0.02659903,0.00008057327,0.00005194524,0.00003161499,0.00007908422,0.000003605485,0.000009281474],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9855098,"threshold_uncertainty_score":0.4548161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07268246504518185,"score_gpt":0.3377939215374597,"score_spread":0.2651114564922779,"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."}}