{"id":"W191266118","doi":"10.17705/1jais.00115","title":"How To Build Enterprise Data Models To Achieve Compliance To Standards Or Regulatory Requirements (and share data).","year":2007,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Computer science; Ontology; Enterprise data management; Semantic Web; Business rule; Enterprise information system; Knowledge management; Software engineering; Data science; Business process; World Wide Web; Business","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":[],"consensus_categories":[],"category_scores_codex":[0.005798574,0.0001036335,0.0002407992,0.0001893687,0.0001766491,0.0008598307,0.002538436,0.00005990844,3.204734e-7],"category_scores_gemma":[0.001525929,0.00006857805,0.00003683844,0.000323207,0.000005132068,0.005716634,0.001007458,0.00008541465,0.000005090294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005341165,"about_ca_system_score_gemma":0.0001487259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001500598,"about_ca_topic_score_gemma":0.00004487756,"domain_scores_codex":[0.9976896,0.00005264025,0.0007287333,0.0001417465,0.001160498,0.0002268048],"domain_scores_gemma":[0.9965619,0.0001885476,0.001092366,0.0009711716,0.001039452,0.0001466064],"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":[0.001194468,0.0000855312,0.007702645,0.0004639964,0.0004970242,0.000003424442,0.02254917,0.01639666,0.0003459596,0.01658244,0.8758801,0.05829857],"study_design_scores_gemma":[0.001821413,0.0005529274,0.01452627,0.001097899,0.00005788998,0.00006352916,0.004140323,0.09307376,0.0003065773,0.0004365958,0.8835018,0.0004210001],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006139855,0.0000498507,0.9828597,0.007242606,0.001881985,0.0007777417,0.0006833858,0.00002741126,0.0003374874],"genre_scores_gemma":[0.9725978,0.000007965384,0.02428438,0.001894074,0.0002849023,0.00000875959,0.00003077622,0.000007642587,0.0008836585],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.966458,"threshold_uncertainty_score":0.8291367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1324379109797287,"score_gpt":0.3481231449114161,"score_spread":0.2156852339316875,"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."}}