{"id":"W4296462254","doi":"10.1101/2022.09.19.508491","title":"Deciphering the genetic structure of the Quebec founder population using genealogies","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"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":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Alliance de recherche numérique du Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Kinship; Founder effect; Genealogy; Population; Identity by descent; Geography; Inbreeding; Peninsula; Colonization; Demography; Settlement (finance); Genetic structure; Endogamy; History; Ethnology; Biology; Haplotype; Archaeology; Genetic variation; Genetics; Anthropology; Sociology","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":[0.0002341363,0.0001146646,0.0001329021,0.001058403,0.0006054724,0.0005252348,0.0002580718,0.0001496374,0.003490196],"category_scores_gemma":[0.0005330025,0.00005535068,0.0001107951,0.001095565,0.0002172644,0.000216619,0.0001413719,0.0002486873,0.0001981398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003307059,"about_ca_system_score_gemma":0.001659041,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.858966,"about_ca_topic_score_gemma":0.9025859,"domain_scores_codex":[0.9999398,0.00001148377,0.000001851041,0.00001974628,0.00001135955,0.00001577816],"domain_scores_gemma":[0.9997867,0.00005264416,0.00004020436,0.00001588565,0.00007715612,0.00002752963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001825653,0.00003971598,0.8337537,0.00009018275,0.0002132116,0.0005375466,0.002006669,0.006773903,0.04829234,0.003895726,0.003171103,0.1010434],"study_design_scores_gemma":[0.000005876946,0.00001779589,0.9794858,0.00003032621,0.0000265494,0.000109295,0.0005424371,0.009840382,0.001798216,0.0002583684,0.007871912,0.00001307407],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892504,0.000594655,0.004801158,0.0001335537,0.000005802292,0.00001859918,0.001805959,0.00004315421,0.003346672],"genre_scores_gemma":[0.9939964,0.0002558285,0.002827283,0.00003443756,0.000002450467,0.000008308029,0.0009762074,0.000009837381,0.001889147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.141034,"threshold_uncertainty_score":0.2837291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01389854289571055,"score_gpt":0.2256352246076183,"score_spread":0.2117366817119078,"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."}}