{"id":"W4407407173","doi":"10.1038/d41586-025-00091-6","title":"Genetic data from Indigenous Greenlanders could help to narrow health-care gap","year":2025,"lang":"en","type":"article","venue":"Nature","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Indigenous; Genetic data; Health care; Geography; Environmental health; Medicine; Political science; Biology; Ecology; Population","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004213401,0.0007029813,0.0009623268,0.00240831,0.001622748,0.001086278,0.0011536,0.0008549783,0.006875721],"category_scores_gemma":[0.007924296,0.0001724679,0.0003529784,0.002532433,0.001015494,0.001429995,0.001603214,0.001191396,0.0007804403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001240789,"about_ca_system_score_gemma":0.002749286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1501772,"about_ca_topic_score_gemma":0.1713988,"domain_scores_codex":[0.9990242,0.0004365045,0.00008730943,0.0001696848,0.0000977259,0.0001846475],"domain_scores_gemma":[0.994753,0.002117645,0.0007122112,0.0005889367,0.001266392,0.0005618191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004458729,0.001111099,0.7620476,0.0009284972,0.0003658655,0.003572871,0.01452032,0.0007124675,0.01230962,0.009288576,0.01308811,0.181609],"study_design_scores_gemma":[0.0001310333,0.0005404007,0.8838879,0.001693737,0.0004252058,0.001333692,0.01940904,0.0006835716,0.002163758,0.0195499,0.07006572,0.0001161403],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9237576,0.004416467,0.01049058,0.01736554,0.0005409239,0.0003014648,0.005539696,0.0001056934,0.03748209],"genre_scores_gemma":[0.9513937,0.007836503,0.0141128,0.01156077,0.0004288097,0.0003527955,0.00411198,0.00006777434,0.010135],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1501772,"threshold_uncertainty_score":0.2986061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01422786524626224,"score_gpt":0.3107567731379909,"score_spread":0.2965289078917287,"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."}}