{"id":"W2356803596","doi":"","title":"Application of Data Warehouse in Commercial Banks","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data warehouse; Computer science; Dimensional modeling; Data transformation; Process (computing); Data flow diagram; Competition (biology); Credit card; Architecture; Warehouse; Database; Data science; World Wide Web; Business; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002100519,0.0001483231,0.0002174843,0.0001852777,0.0001704585,0.00003609743,0.00343939,0.00007096308,0.000003156529],"category_scores_gemma":[9.919331e-7,0.0001606905,0.00003804594,0.001010019,0.0001107171,0.0006121381,0.001111391,0.0001596898,0.0001014723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003332578,"about_ca_system_score_gemma":0.0000911588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002033105,"about_ca_topic_score_gemma":0.00004063447,"domain_scores_codex":[0.9983893,0.00003083926,0.0004757297,0.0006936334,0.0001749143,0.0002356015],"domain_scores_gemma":[0.997321,0.00009941494,0.0001600455,0.002256998,0.00008618253,0.00007639071],"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":[0.000001187833,0.0003716783,0.002154468,0.00001303386,0.000008589484,0.000001294559,0.0003562656,0.00007179845,0.001119049,0.01538559,0.004105079,0.9764119],"study_design_scores_gemma":[0.0006652445,0.00002776606,0.07126696,0.0000162257,0.00000922831,0.0000765345,0.000009329156,0.2023162,0.001620945,0.001278841,0.7223183,0.0003944843],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002502337,0.0001018633,0.9954636,0.0006635981,0.00001047605,0.0006393248,0.0001285391,0.0001605106,0.0003298234],"genre_scores_gemma":[0.1404431,0.00004972619,0.8582773,0.0002418928,0.00009977812,0.0004736195,0.0003670733,0.00001511339,0.0000324958],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9760175,"threshold_uncertainty_score":0.6552767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04650231801150504,"score_gpt":0.2897995692173535,"score_spread":0.2432972512058485,"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."}}