{"id":"W4362555731","doi":"10.1038/s41431-023-01356-2","title":"Deciphering the genetic structure of the Quebec founder population using genealogies","year":2023,"lang":"en","type":"article","venue":"European Journal of Human Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean; Université du Québec à Chicoutimi","funders":"Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Kinship; Founder effect; Population; Genealogy; Identity by descent; Geography; Inbreeding; Demography; Colonization; Genetic structure; Settlement (finance); Peninsula; Population genetics; Biology; Evolutionary biology; History; Haplotype; Genetics; Genetic variation; Archaeology; Anthropology; Sociology; Allele","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":[],"consensus_categories":[],"category_scores_codex":[0.0002065388,0.0001419488,0.0001352484,0.00004059698,0.0002057534,0.00003256509,0.0006229017,0.00004351254,0.00001411778],"category_scores_gemma":[0.00004607183,0.00008799955,0.0001425346,0.0001422343,0.0001582959,0.000002925045,0.0002273693,0.0001461935,0.000001529278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001384017,"about_ca_system_score_gemma":0.00006341979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002196761,"about_ca_topic_score_gemma":0.0001423942,"domain_scores_codex":[0.9986354,0.0003299892,0.000471781,0.0001360638,0.0002436006,0.0001831882],"domain_scores_gemma":[0.9989809,0.0000163861,0.0004196491,0.0004117922,0.0001284108,0.00004287271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000246447,0.00001972441,0.03009618,0.00002782546,0.0001312976,0.000003738392,0.0006492331,0.1804627,0.7809138,0.0002372151,0.0007838375,0.006649752],"study_design_scores_gemma":[0.0002974772,0.0002213347,0.9788493,0.00002953885,0.00006739059,0.00006167674,0.0002273563,0.00006484266,0.01787807,0.001149093,0.001036231,0.0001177219],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956459,0.0009372343,0.002655323,0.0000413698,0.0004797639,0.0001015861,0.000006463923,0.000004847084,0.0001275167],"genre_scores_gemma":[0.9915245,0.00003565672,0.00754778,0.00006293554,0.0006044515,1.712386e-7,0.000005000274,0.00003757414,0.0001819496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9487531,"threshold_uncertainty_score":0.3588518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02856448843909712,"score_gpt":0.2643459679471533,"score_spread":0.2357814795080561,"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."}}