{"id":"W2804792234","doi":"10.23889/ijpds.v3i1.450","title":"Unlocking First Nations health information through data linkage","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Laurentian University; Institute for Work & Health; Sunnybrook Health Science Centre; Institute for Clinical Evaluative Sciences","funders":"Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Indigenous; Linkage (software); Sovereignty; Context (archaeology); Linked data; Population; Record linkage; Geography; Metis; Economic growth; Database; Political science; Medicine; Environmental health; Biology; Law; Genetics; Ecology; World Wide Web; Computer science; Economics; Politics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.01502152,0.0001221754,0.0001615144,0.0009094535,0.002522614,0.003616138,0.0125341,0.00003494312,0.0002236115],"category_scores_gemma":[0.0220649,0.0001002084,0.0000395053,0.001358432,0.0003168304,0.03518074,0.003443755,0.0001451329,0.0002870373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002293958,"about_ca_system_score_gemma":0.0004263196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005746934,"about_ca_topic_score_gemma":0.003017335,"domain_scores_codex":[0.9939378,0.00008994499,0.001325466,0.0006144062,0.003702622,0.0003296967],"domain_scores_gemma":[0.9936618,0.0007339417,0.001183662,0.002199484,0.002058266,0.00016286],"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":[0.00008047523,0.0001453798,0.004913412,0.00001760362,0.0000661957,0.000002300859,0.002133854,0.001027358,0.00001338745,0.2939061,0.3931488,0.3045452],"study_design_scores_gemma":[0.0003572988,0.00005303313,0.0108972,0.00005510094,0.000006018186,0.00003032027,0.0005465501,0.08033777,0.00001047129,0.0213006,0.8862786,0.0001270181],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002437791,0.00003791177,0.946075,0.03202141,0.0105087,0.0004537101,0.005367042,0.00005769749,0.003040721],"genre_scores_gemma":[0.9155758,0.0001838836,0.06947912,0.004671929,0.001823865,0.000007279039,0.007868689,0.000009990669,0.0003794436],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.913138,"threshold_uncertainty_score":0.998776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4724617458589112,"score_gpt":0.5651324642231068,"score_spread":0.09267071836419566,"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."}}