{"id":"W2163438246","doi":"10.14778/1687553.1687556","title":"StatAdvisor","year":2009,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); University of Waterloo","funders":"","keywords":"Computer science; IBM; Matching (statistics); SQL; Data mining; Oracle; Key (lock); Plan (archaeology); Construct (python library); Workload; Query plan; Information retrieval; Database; Statistics; Software engineering; Web search query; Mathematics; Sargable; Search engine; 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":[0.0001436067,0.00009072247,0.0001177168,0.00003357229,0.00008157266,0.00002825582,0.0006073239,0.00001543629,0.000003622939],"category_scores_gemma":[0.00003555743,0.00005613629,0.00005518884,0.0002186216,0.00002880229,0.0005249464,0.0002210821,0.00006042806,0.000006862375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002627598,"about_ca_system_score_gemma":0.00001516318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001074181,"about_ca_topic_score_gemma":4.957266e-7,"domain_scores_codex":[0.9991847,0.000002501413,0.0001874357,0.0001903454,0.0002585685,0.0001764496],"domain_scores_gemma":[0.9995141,0.00001008502,0.0001495427,0.0001946109,0.00009044108,0.00004119708],"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":[0.000004979741,0.00004753347,0.0002154073,0.00002086963,0.000006545154,3.082437e-7,0.0004032422,0.000006577815,0.03671016,0.9444947,0.003323003,0.01476671],"study_design_scores_gemma":[0.0008806598,0.0004020905,0.008882077,0.0002371103,0.00001497516,0.00004771358,0.0003821462,0.00107291,0.6723152,0.05298606,0.262354,0.0004251248],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3110972,0.002142066,0.4976836,0.03613176,0.002805487,0.00356352,0.00008992795,0.001196905,0.1452895],"genre_scores_gemma":[0.924416,0.00002932407,0.07431532,0.0004636242,0.000055587,0.00001473478,4.564326e-7,0.000004603432,0.0007003699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8915086,"threshold_uncertainty_score":0.2289171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00712529554645085,"score_gpt":0.2147507865872517,"score_spread":0.2076254910408009,"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."}}