{"id":"W2890629418","doi":"10.23889/ijpds.v3i4.872","title":"Leveraging best practices in data governance: An organization-wide data inventory and mapping project to support a five year data strategy","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"College of Physicians and Surgeons of Ontario","funders":"","keywords":"Data governance; Data quality; Data warehouse; Computer science; Enterprise data management; Data management; Workflow; Metadata; Data dictionary; Data element; Data virtualization; Information governance; Data science; Database; World Wide Web; Business; Information system; Engineering; Management information systems; Marketing","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":["metaresearch","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.01856695,0.0001459483,0.0001847477,0.0006010346,0.0005112174,0.003698183,0.02336952,0.00003753657,0.0001685616],"category_scores_gemma":[0.04225088,0.0001287928,0.000007608485,0.00140041,0.0002271643,0.03606953,0.01567629,0.0001805849,0.00006658001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001270362,"about_ca_system_score_gemma":0.0007741122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002678365,"about_ca_topic_score_gemma":0.006539779,"domain_scores_codex":[0.9941772,0.0001723342,0.0009574836,0.001713256,0.002669987,0.0003097466],"domain_scores_gemma":[0.9916586,0.0004807437,0.001313971,0.005265769,0.001082936,0.0001980396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002166619,0.0005121354,0.2847915,0.0000277356,0.0001053328,0.00006103391,0.003127293,0.0004329107,0.0002821121,0.008885178,0.4568147,0.2447434],"study_design_scores_gemma":[0.0009094501,0.0001623447,0.1326393,0.0001490345,0.0000326915,0.0001062297,0.007944775,0.2893986,0.00001155523,0.003578179,0.5646493,0.0004185213],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6255275,0.0001420787,0.2684773,0.02661927,0.01195593,0.002764004,0.06250773,0.0001163686,0.00188983],"genre_scores_gemma":[0.9213629,0.0001720303,0.04399578,0.001636655,0.001253701,0.0000040029,0.03060171,0.00002496374,0.0009482536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2958354,"threshold_uncertainty_score":0.9973361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6289315489095745,"score_gpt":0.5585743379486544,"score_spread":0.07035721096092007,"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."}}