{"id":"W2970459958","doi":"10.1038/s41588-019-0484-x","title":"A method for genome-wide genealogy estimation for thousands of samples","year":2019,"lang":"en","type":"article","venue":"Nature Genetics","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":597,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Engineering and Physical Sciences Research Council; Missouri Soybean Merchandising Council; Research Councils UK; Wellcome Trust; Wellcome","keywords":"Biology; Introgression; Selection (genetic algorithm); Evolutionary biology; Genome; 1000 Genomes Project; Natural selection; Haplotype; Population; Directional selection; Genetics; Human genome; Allele; Genetic variation; Genotype; Gene; Single-nucleotide polymorphism; Artificial intelligence; Demography; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003925434,0.0001478479,0.0002241498,0.00006117981,0.00004537076,0.00001173785,0.0002445293,0.0004734257,0.00001523741],"category_scores_gemma":[0.00020591,0.0001355281,0.0001580752,0.00008028918,0.00005439234,0.000001371028,0.00007735909,0.0001082633,0.000002774051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009925097,"about_ca_system_score_gemma":0.0001352549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000290428,"about_ca_topic_score_gemma":0.00001290938,"domain_scores_codex":[0.9988782,0.00003850507,0.0002532488,0.0003732556,0.0001568943,0.0002998352],"domain_scores_gemma":[0.9989491,0.0001244037,0.0001067675,0.0004157647,0.0003411141,0.00006292363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007967152,0.00009383867,0.012043,0.000527097,0.0002665744,3.078073e-7,0.0001246433,0.009926078,0.8889316,0.0008499181,0.01064746,0.07579277],"study_design_scores_gemma":[0.001848502,0.001763141,0.01237394,0.00001192161,0.00006517675,0.000007920376,0.00005443823,0.0136976,0.7038966,0.003678257,0.262299,0.0003035615],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4637447,0.004391184,0.5297675,0.000173667,0.0002351412,0.00114938,0.0002114576,0.00000692011,0.000320077],"genre_scores_gemma":[0.5164733,0.0002176222,0.4813437,0.0002869286,0.0002114428,0.00007341026,0.0005835885,0.00003415891,0.00077583],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2516515,"threshold_uncertainty_score":0.5526674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0138094166625949,"score_gpt":0.3403765262111521,"score_spread":0.3265671095485572,"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."}}