{"id":"W2474544103","doi":"10.18260/p.26272","title":"Architectural Evaluation of Master Data Management (MDM): Literature Review","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"St. Jude Medical; American Society for Engineering Education","keywords":"Computer science; Notation; Master data; Process (computing); Qualitative property; Quantitative analysis (chemistry); Software engineering; Data science; Business process; Management science; Process management; Knowledge management; Engineering; Data mining; Work in process; Operations management; Programming language","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01513695,0.00009672887,0.0002041781,0.0001535671,0.00002887091,0.0001129192,0.001876218,0.00002087589,0.004622241],"category_scores_gemma":[0.001234066,0.00004349085,0.00005333149,0.0004896189,0.00005161776,0.0007774194,0.001394686,0.00003446527,0.0005870805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001650157,"about_ca_system_score_gemma":0.00001387656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002557449,"about_ca_topic_score_gemma":0.00002589079,"domain_scores_codex":[0.9956298,0.0005502454,0.0006296258,0.0005303316,0.002516161,0.0001438196],"domain_scores_gemma":[0.9964472,0.0002739218,0.0001994105,0.002732011,0.0003001917,0.00004726792],"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":[0.000006555886,0.00001964404,0.00002879663,0.0001369317,0.00002153117,0.000001532475,0.00003326693,8.073332e-7,0.00001537826,0.01089065,0.1752933,0.8135516],"study_design_scores_gemma":[0.0008964294,0.00004159977,0.004855077,0.003613653,0.0002059934,0.000006960669,0.0001669413,0.0004993097,0.00009197069,0.05274442,0.9366711,0.000206483],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.006099854,0.0470502,0.2294146,0.1005123,0.001920984,0.0058701,0.001835052,0.0001678949,0.607129],"genre_scores_gemma":[0.6272053,0.04198771,0.05045753,0.03116272,0.0004749238,0.0002306198,0.001433479,0.00005617274,0.2469916],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8133451,"threshold_uncertainty_score":0.9962876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5556549298580707,"score_gpt":0.5130888725810021,"score_spread":0.04256605727706864,"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."}}