{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003215031,0.0001342145,0.0001504592,0.001535563,0.0007761162,0.0007000276,0.0003580402,0.0001719644,0.002886192],"category_scores_gemma":[0.001241511,0.00007663044,0.0001426601,0.001750156,0.000297601,0.0003919319,0.0001948003,0.0003391091,0.0002096379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005427808,"about_ca_system_score_gemma":0.002958999,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9421587,"about_ca_topic_score_gemma":0.9633753,"domain_scores_codex":[0.9998907,0.00002212192,0.000004440405,0.00003372791,0.00001987336,0.00002916593],"domain_scores_gemma":[0.9994918,0.000121696,0.00008341227,0.00003050857,0.0002149304,0.00005764062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009794036,0.000024187,0.911265,0.00005281113,0.0001183672,0.0004329769,0.003172355,0.003905865,0.007526353,0.002291987,0.001924238,0.06918795],"study_design_scores_gemma":[0.000004501423,0.00002001781,0.9856354,0.00003146598,0.00002641103,0.00009245808,0.001073851,0.006826036,0.0005119473,0.000211632,0.005552493,0.00001383411],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930645,0.0005471504,0.002330935,0.0001516741,0.000004996839,0.00001989971,0.001332123,0.00002702949,0.002521716],"genre_scores_gemma":[0.9950259,0.0003354941,0.002255189,0.00003267494,0.000003049227,0.00000922807,0.0009494516,0.000008682821,0.001380206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0578413,"threshold_uncertainty_score":0.1163638,"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."}}