{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006488411,0.0002707062,0.0002804912,0.0001258798,0.0003329179,0.0001563394,0.001219635,0.00007929545,0.00003272077],"category_scores_gemma":[0.0003916248,0.0002080877,0.00004499053,0.0003279545,0.00004651503,0.001216122,0.000656691,0.0001280676,0.0000461653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006765551,"about_ca_system_score_gemma":0.000100331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005697723,"about_ca_topic_score_gemma":0.00000971867,"domain_scores_codex":[0.9977254,0.000223735,0.000449857,0.0008163533,0.0003579409,0.0004267743],"domain_scores_gemma":[0.9973828,0.0001310831,0.0001282759,0.001979231,0.0001497535,0.0002289253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007843364,0.0008508,0.003467713,0.0001823597,0.0002291999,0.0004011079,0.005801613,0.04360616,0.3014646,0.5165958,0.1098609,0.01746131],"study_design_scores_gemma":[0.002255441,0.001827049,0.002197043,0.0003735075,0.00005821563,0.0001217716,0.001078775,0.06841493,0.1543148,0.009468189,0.7552515,0.004638804],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01314918,0.00006419829,0.9853671,0.00004039262,0.0003764422,0.0006367845,0.0000469545,0.0002531604,0.00006585316],"genre_scores_gemma":[0.1618806,0.000001779008,0.8374262,0.0002663989,0.0001156368,0.00003007097,0.00002184849,0.00002899296,0.0002284102],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6453906,"threshold_uncertainty_score":0.8485572,"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."}}