{"id":"W2989842504","doi":"10.23889/ijpds.v4i3.1311","title":"Alberta's Data and Analytic Strategy: Leveraging Linked Data to Drive Innovation","year":2019,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Health","funders":"","keywords":"Business; Data access; Stakeholder; Data quality; Raw data; Health data; Health care; Public relations; Marketing; Computer science; Database; Economics; Political science; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06438223,0.0009321416,0.0006934406,0.009750172,0.00716674,0.02047378,0.006389843,0.002792557,0.007968705],"category_scores_gemma":[0.1253117,0.0007810169,0.001192075,0.01447585,0.005982765,0.006032204,0.01485545,0.00397586,0.002830296],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05901176,"about_ca_system_score_gemma":0.2567413,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8495692,"about_ca_topic_score_gemma":0.8538535,"domain_scores_codex":[0.9529741,0.01669889,0.002265946,0.003277612,0.02095679,0.003826697],"domain_scores_gemma":[0.8625762,0.04247891,0.004011986,0.0170728,0.05433409,0.01952607],"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":[0.0001819109,0.0001254263,0.02271898,0.0007634014,0.0001824864,0.0004156348,0.00377235,0.004977095,0.001024417,0.2143858,0.4794028,0.2720497],"study_design_scores_gemma":[0.00009464834,0.00006994676,0.009941952,0.001965864,0.0001104714,0.0001032443,0.00443477,0.01090338,0.0014204,0.07999441,0.8908018,0.0001591063],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.02403454,0.008766721,0.1264078,0.5502439,0.00324603,0.002533117,0.0382473,0.009542204,0.2369785],"genre_scores_gemma":[0.2212577,0.0121425,0.6101865,0.04455681,0.001552218,0.00149146,0.05015821,0.002239457,0.05641506],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9409882,"threshold_uncertainty_score":0.4281623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6701320095536344,"score_gpt":0.6303662328664161,"score_spread":0.03976577668721826,"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."}}