{"id":"W4408531404","doi":"10.1016/j.gimo.2025.102632","title":"O63: Leveraging global and local genomic data to guide equitable carrier screening: From Genome Aggregation Database (gnomAD) to Qatar Genome","year":2025,"lang":"en","type":"article","venue":"Genetics in Medicine Open","topic":"Biological Research and Disease Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Genome; Database; Computational biology; Genetics; Biology; Computer science; Gene","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":[0.004924333,0.0008838915,0.0009265207,0.003656343,0.0008758457,0.003074037,0.001617563,0.001129479,0.01873751],"category_scores_gemma":[0.01673461,0.0005499169,0.0006960939,0.005171117,0.0003509235,0.002016611,0.004075048,0.001005095,0.00931237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001386849,"about_ca_system_score_gemma":0.004044465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04131173,"about_ca_topic_score_gemma":0.03517336,"domain_scores_codex":[0.9989134,0.0002283051,0.0001371027,0.0003070988,0.000275833,0.0001383811],"domain_scores_gemma":[0.9932252,0.001859187,0.000674431,0.001867279,0.001740582,0.0006333733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001670045,0.0003180548,0.04919813,0.000844728,0.0002703966,0.0006256375,0.001695248,0.0067504,0.01235417,0.009841479,0.6865453,0.2298864],"study_design_scores_gemma":[0.001053147,0.0003781711,0.07003973,0.0008122543,0.0004150792,0.0006935306,0.002385825,0.04885172,0.02029773,0.04622346,0.8085073,0.0003420113],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.06824687,0.001335839,0.1221054,0.009282106,0.000550993,0.0008501892,0.6826596,0.08078446,0.0341846],"genre_scores_gemma":[0.1479958,0.001006444,0.2093071,0.002267451,0.0001974093,0.0004160995,0.625636,0.006571012,0.006602621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04131173,"threshold_uncertainty_score":0.08214253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08698375788357154,"score_gpt":0.3964424041386906,"score_spread":0.3094586462551191,"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."}}