{"id":"W2402498022","doi":"","title":"Cardinality estimation using neural networks","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); York University; University of Waterloo","funders":"","keywords":"Cardinality (data modeling); Estimator; Computer science; Bounded function; Artificial neural network; Range (aeronautics); Column (typography); Algorithm; Data mining; Theoretical computer science; Artificial intelligence; Mathematics; Statistics","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":[0.001098497,0.0001249635,0.0001282827,0.0001585912,0.0001269086,0.0005023266,0.0008553343,0.00003436782,1.207435e-7],"category_scores_gemma":[0.0002739784,0.0001231951,0.00001818909,0.0007217527,0.00009410084,0.001709538,0.000823183,0.000106594,7.767732e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001002296,"about_ca_system_score_gemma":0.00009237914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002401856,"about_ca_topic_score_gemma":2.094444e-7,"domain_scores_codex":[0.9987708,0.00001670416,0.0001403284,0.0003951544,0.0003726766,0.0003043632],"domain_scores_gemma":[0.999041,0.00006246389,0.00003871514,0.0004704103,0.0001766827,0.0002107773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[6.9267e-7,0.000008494232,0.00211587,0.00001045655,0.00000354939,0.00001755741,0.0003749687,0.7384527,0.00003405179,0.001957014,0.0002497735,0.2567748],"study_design_scores_gemma":[0.00005960838,0.00003404263,0.001589322,0.00002098825,0.000002121414,0.00007372801,0.000001505168,0.9977146,0.0001314348,0.0001387488,0.00008738355,0.0001464558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06685996,0.00008140155,0.9316941,0.00002964312,0.0005871691,0.00006396235,6.100063e-7,0.0006791942,0.000003992298],"genre_scores_gemma":[0.4374268,7.237877e-7,0.5624659,0.00004499072,0.00005493762,0.000001841312,6.858579e-7,0.000003653411,4.331169e-7],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3705669,"threshold_uncertainty_score":0.5023751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.031090544868678,"score_gpt":0.2570836142534774,"score_spread":0.2259930693847994,"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."}}