{"id":"W2156647424","doi":"10.1109/icde.1994.282996","title":"A query sampling method for estimating local cost parameters in a multidatabase system","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Sampling (signal processing); Query optimization; Class (philosophy); Sample (material); Data mining; Artificial intelligence; Detector","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":[],"consensus_categories":[],"category_scores_codex":[0.003353364,0.000697227,0.001093731,0.001532543,0.0006579412,0.0009394183,0.001638693,0.0007398577,0.001177492],"category_scores_gemma":[0.01254365,0.0004401237,0.000651402,0.001563143,0.0005568349,0.002448918,0.0009514336,0.0009212045,0.0003168164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131003,"about_ca_system_score_gemma":0.00107107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008076265,"about_ca_topic_score_gemma":0.005545526,"domain_scores_codex":[0.9968992,0.001140909,0.0001456941,0.0004735724,0.001192904,0.0001476138],"domain_scores_gemma":[0.9944149,0.002957177,0.0004107283,0.0009170904,0.001123868,0.0001761736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006782745,0.0003646556,0.017229,0.0001718691,0.0002446557,0.0001747016,0.0004307192,0.4674081,0.01947812,0.02241758,0.002599004,0.4688033],"study_design_scores_gemma":[0.00001796473,0.00004576548,0.001197023,0.000002934928,0.00001603788,0.00005047414,0.00002366268,0.9921075,0.002861254,0.003071947,0.0005917974,0.00001367415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0137906,0.00007268167,0.9855678,0.00003362768,0.000005132489,0.00004322329,0.0000305826,0.0003224201,0.0001340222],"genre_scores_gemma":[0.3838249,0.0001173126,0.6144077,0.00007185056,0.00005225291,0.0003080801,0.0003848072,0.0001743017,0.0006587197],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008076265,"threshold_uncertainty_score":0.01773447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08598551938416749,"score_gpt":0.3234501232458155,"score_spread":0.237464603861648,"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."}}