{"id":"W1551621452","doi":"10.1007/bf03405313","title":"Using Data Linkage to Identify First Nations Manitobans: Technical, Ethical, and Political Issues","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Census and Population Estimation","field":"Mathematics","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; Manitoba Health","funders":"Canadian Institutes of Health Research","keywords":"Record linkage; Population; Linkage (software); Medicine; Cancer registry; Metis; Geography; Demography; Gerontology; Database; Environmental health; Sociology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1566244,0.0006437264,0.001167547,0.004400002,0.004528413,0.006408839,0.003179639,0.002356859,0.002043801],"category_scores_gemma":[0.3100546,0.0006976045,0.0005356715,0.01130015,0.003152181,0.004699489,0.00412342,0.002445433,0.0008061423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003226248,"about_ca_system_score_gemma":0.01192405,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1022049,"about_ca_topic_score_gemma":0.1303639,"domain_scores_codex":[0.8397425,0.1371074,0.00674696,0.002639819,0.01121316,0.002550098],"domain_scores_gemma":[0.7457231,0.1689258,0.01963707,0.03020697,0.03282613,0.002680867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004743859,0.0003352054,0.4164741,0.001681269,0.0006847307,0.0008831154,0.0674659,0.002944306,0.001512327,0.1296135,0.08165735,0.2962738],"study_design_scores_gemma":[0.0002994272,0.0003259671,0.3120761,0.01093177,0.000588549,0.001906551,0.1667109,0.02195881,0.008161626,0.1739229,0.3025747,0.0005425916],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4526626,0.01131649,0.20973,0.196355,0.003114585,0.003021471,0.006985829,0.0006546745,0.1161594],"genre_scores_gemma":[0.7660223,0.002911849,0.2004211,0.01330767,0.0003793777,0.004087086,0.00183971,0.0001984089,0.0108324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8977951,"threshold_uncertainty_score":0.8283188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3668122836441599,"score_gpt":0.4899932972303239,"score_spread":0.1231810135861641,"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."}}