{"id":"W4240633503","doi":"10.21203/rs.2.20017/v1","title":"Genetic profiling of 2,683 Vietnamese genomes from non-invasive prenatal testing data","year":2020,"lang":"en","type":"preprint","venue":"Research Square (Research Square)","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vietnamese; Profiling (computer programming); Computational biology; Biology; Genome; Genetics; Computer science; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0006239637,0.0002882688,0.0002340296,0.001563218,0.000324849,0.0005171049,0.0002563256,0.0002217794,0.001400741],"category_scores_gemma":[0.002226535,0.0001459167,0.0003990508,0.001869167,0.0001352022,0.000118169,0.0004858641,0.0002526852,0.0002668407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003405761,"about_ca_system_score_gemma":0.000516351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01170764,"about_ca_topic_score_gemma":0.0146274,"domain_scores_codex":[0.9995301,0.0001062177,0.00003840565,0.0001555778,0.0001058877,0.00006388096],"domain_scores_gemma":[0.9987795,0.0005827043,0.0002267808,0.0001185051,0.0001788989,0.0001135166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006724196,0.0001230399,0.8660004,0.0004470822,0.0005788957,0.003343494,0.001208625,0.002949516,0.07296868,0.0004602179,0.002842056,0.04840562],"study_design_scores_gemma":[0.00003483637,0.00009704042,0.9717028,0.00005891055,0.0001534196,0.001833351,0.0006021648,0.006245332,0.01089107,0.0003657273,0.007996614,0.00001881853],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844075,0.0003338752,0.001478972,0.0001012437,0.000007762948,0.00002955631,0.01311405,0.00004419697,0.0004829389],"genre_scores_gemma":[0.9507468,0.0002932653,0.005795257,0.00008710391,0.00001132621,0.0000411429,0.04243444,0.00003109908,0.0005596274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01170764,"threshold_uncertainty_score":0.02327895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1557903199865121,"score_gpt":0.3895504407007536,"score_spread":0.2337601207142415,"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."}}