{"id":"W35014963","doi":"10.2196/27631","title":"Using Design Guidelines to Improve Data Warehouse Logical Design.","year":2003,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Logical data model; Data warehouse; Schema (genetic algorithms); Conceptual schema; Dimensional modeling; Software versioning; Logical conjunction; Software engineering; Data modeling; Database; Data science; Data mining; Information retrieval; Programming language; Software","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09641461,0.00222246,0.001091965,0.004423594,0.002026416,0.0114313,0.004943182,0.002309023,0.006030626],"category_scores_gemma":[0.1904079,0.003844048,0.003513748,0.003764754,0.002171177,0.01050014,0.004596235,0.004374571,0.003722745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002137797,"about_ca_system_score_gemma":0.01045424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007720006,"about_ca_topic_score_gemma":0.01267035,"domain_scores_codex":[0.9108257,0.05626176,0.01375001,0.003155896,0.01469313,0.001313588],"domain_scores_gemma":[0.8012142,0.1060349,0.008691045,0.03230037,0.0493841,0.002375403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001010662,0.0007778849,0.008051193,0.005580836,0.001052349,0.001053149,0.008981588,0.0350227,0.01177213,0.1357739,0.144309,0.6466146],"study_design_scores_gemma":[0.001297785,0.0008819752,0.002485417,0.004183662,0.00135413,0.001226936,0.003581609,0.2299837,0.02542636,0.2036895,0.5254703,0.0004186105],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002371584,0.0004336038,0.9796826,0.001507376,0.0001550426,0.002527432,0.001224382,0.009761827,0.002336022],"genre_scores_gemma":[0.009146878,0.0002007897,0.9855369,0.0003378892,0.00003037773,0.001209044,0.001810299,0.0009318669,0.0007959324],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09641461,"threshold_uncertainty_score":0.5098953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4361212398271037,"score_gpt":0.4194635940573503,"score_spread":0.01665764576975343,"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."}}