{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003066583,0.0002106017,0.0002906309,0.0001816202,0.0001171809,0.00001382071,0.000484872,0.0001654552,0.00003533015],"category_scores_gemma":[0.00005135824,0.0002461612,0.00004903382,0.0002132425,0.0001935188,0.000002508575,0.0002905362,0.000244966,8.361798e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001027451,"about_ca_system_score_gemma":0.0003660598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001152969,"about_ca_topic_score_gemma":0.003537771,"domain_scores_codex":[0.9982385,0.0002111623,0.0003844289,0.0004778094,0.0001924025,0.0004956959],"domain_scores_gemma":[0.9992807,0.00002493298,0.0001124768,0.0003737935,0.00003494974,0.0001731653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000045054,0.000109762,0.9784917,0.00002479896,0.00001677924,0.00000298897,0.0001586325,0.006032357,0.007409478,0.00003340078,0.0031368,0.004538314],"study_design_scores_gemma":[0.0009639923,0.0004702167,0.9801442,0.00001092536,0.00001662786,0.000002015109,0.0002408793,0.0005661581,0.005912127,0.0006045172,0.01075727,0.0003110758],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9630649,0.008563769,0.02698568,0.00005929509,0.0002816709,0.0003407216,0.0006012708,0.000005670627,0.000097],"genre_scores_gemma":[0.9637755,0.0003064928,0.03531777,0.0002065909,0.00003521687,0.00003725544,0.0002265441,0.00001994717,0.00007475811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008332088,"threshold_uncertainty_score":0.999999,"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."}}