{"id":"W4205681907","doi":"10.3389/fgene.2021.808829","title":"Correspondence Between Genomic- and Genealogical/Coalescent-Based Inference of Homozygosity by Descent in Large French-Canadian Genealogies","year":2022,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hôpital Maisonneuve-Rosemont; Université du Québec à Chicoutimi; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Ottawa; Compute Canada; Canarie","keywords":"Coalescent theory; Most recent common ancestor; Identity by descent; Biology; Runs of Homozygosity; Founder effect; Genetics; Population; Inbreeding; Evolutionary biology; Inference; Single-nucleotide polymorphism; Genome; Phylogenetics; Haplotype; Gene; Allele; Computer science; Artificial intelligence; Genotype","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":[0.002484418,0.0004971495,0.0006752168,0.00133581,0.002199365,0.001243447,0.002023587,0.0009306991,0.00167796],"category_scores_gemma":[0.00901601,0.0004307377,0.00102015,0.001608567,0.001385353,0.0004870649,0.0006708349,0.0008516192,0.0001526123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.010524,"about_ca_system_score_gemma":0.00667181,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.877212,"about_ca_topic_score_gemma":0.8627208,"domain_scores_codex":[0.9992968,0.0002492997,0.00002468178,0.00016404,0.00009856257,0.0001666833],"domain_scores_gemma":[0.9956509,0.002633212,0.0002296841,0.000261254,0.0008240129,0.0004008447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002458124,0.00006324144,0.09984912,0.00007060033,0.0003180561,0.0003676132,0.0008137639,0.8829413,0.001705312,0.006462881,0.001810379,0.005351937],"study_design_scores_gemma":[0.0001257651,0.00005728386,0.06861634,0.00003862945,0.0001279861,0.00009734653,0.0005865531,0.9235036,0.0007721014,0.003165596,0.002807562,0.0001012839],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939688,0.0002402521,0.003057302,0.0002416806,0.000008705019,0.00002106636,0.0007457499,0.0001171478,0.001599318],"genre_scores_gemma":[0.9941968,0.0001103463,0.003516735,0.00008346846,0.00000524714,0.00002276521,0.001592932,0.00003792521,0.0004339152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.122788,"threshold_uncertainty_score":0.2470222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01052976238751326,"score_gpt":0.2242668023956816,"score_spread":0.2137370400081683,"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."}}