{"id":"W3097829611","doi":"10.1038/s41598-020-76245-5","title":"Genetic profiling of Vietnamese population from large-scale genomic analysis of non-invasive prenatal testing data","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vietnamese; Population; Allele; Genetics; Biology; Allele frequency; Single-nucleotide polymorphism; Genotype; Genome-wide association study; Disease; Ancestry-informative marker; Computational biology; Medicine; Gene; Environmental health","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":[0.0004820624,0.0001111319,0.0004450939,0.0001791191,0.00007740248,0.00003049827,0.0001871507,0.00005998958,0.00004229429],"category_scores_gemma":[0.002555239,0.0001010972,0.0001065767,0.001307608,0.00008913127,0.0001094373,0.0003349051,0.00009563641,0.000003005396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002088758,"about_ca_system_score_gemma":0.0001904185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004983327,"about_ca_topic_score_gemma":0.00007891288,"domain_scores_codex":[0.9978421,0.00002650198,0.0007325968,0.0007209353,0.0004894705,0.0001884492],"domain_scores_gemma":[0.9976792,0.0001459598,0.0006044057,0.001178283,0.000223358,0.000168751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002628442,0.00006785245,0.8755071,0.00009894371,0.0003387203,0.0002174231,0.0005576462,0.002605575,0.1194298,5.513974e-7,0.0001875086,0.0009625523],"study_design_scores_gemma":[0.0002827673,0.00007601902,0.7116367,0.0001967457,0.002587616,0.00002228284,0.0002350719,0.2268366,0.05790743,0.00007845293,0.00001703768,0.0001233147],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947307,0.000230786,0.003802484,0.00003753824,0.0003465092,0.0002943272,0.0003894405,0.00003226098,0.0001359562],"genre_scores_gemma":[0.9474388,0.00000241276,0.04834852,0.00001879575,0.00007047917,0.000003160779,0.00408843,0.00001143525,0.00001792448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.224231,"threshold_uncertainty_score":0.4122624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04140714834629855,"score_gpt":0.2862096877553721,"score_spread":0.2448025394090736,"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."}}