{"id":"W2966783508","doi":"10.29173/cais1111","title":"Canada’s Health Data Repositories: Challenges of Organization, Discoverability and Access","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Discoverability; Metadata; Interoperability; Open data; Health data; World Wide Web; Computer science; Institutional repository; Linked data; Data science; Health care; Political science; Semantic Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0003717849,0.0001228419,0.0003036083,0.00002220999,0.00004849913,0.0002078283,0.001253292,0.0001062152,0.00000352207],"category_scores_gemma":[0.009970007,0.00009004128,0.00002421318,0.0001167864,0.0002918055,0.0002031247,0.001096369,0.00008549757,5.489396e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001748542,"about_ca_system_score_gemma":0.001111778,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02138558,"about_ca_topic_score_gemma":0.006315039,"domain_scores_codex":[0.9989573,0.000019774,0.0003287448,0.0003076122,0.0002162969,0.0001702948],"domain_scores_gemma":[0.9902931,0.00004204164,0.0004914912,0.0003251297,0.008780215,0.0000680574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001298236,0.0001222204,0.9272646,0.001755856,0.0001538903,2.267832e-7,0.005468404,0.000001414141,0.04499089,0.002330609,0.009449455,0.008332651],"study_design_scores_gemma":[0.0006585707,0.0006952811,0.747548,0.0004000637,0.00004830684,0.00002498897,0.005020718,0.00004237807,0.1565576,0.0006062477,0.08808211,0.0003157118],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929996,0.00114483,0.00001357483,0.004817058,0.0001154556,0.0001516453,0.0002369454,0.000005564606,0.0005153787],"genre_scores_gemma":[0.9987974,0.0007232281,0.0001772335,0.000119727,0.00004721755,0.000001863647,0.0000215502,0.000009036838,0.0001027354],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1797166,"threshold_uncertainty_score":0.9983695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03103120629131195,"score_gpt":0.2767233769537491,"score_spread":0.2456921706624371,"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."}}