{"id":"W2912795263","doi":"10.1080/21691401.2018.1533849","title":"Deciphering DMET genetic data: comprehensive assessment of Northwestern Han, Tibetan, Uyghur populations and their comparison to eleven 1000 genome populations","year":2018,"lang":"en","type":"article","venue":"Artificial Cells Nanomedicine and Biotechnology","topic":"Drug Transport and Resistance Mechanisms","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Biology; Genotyping; Genetics; Single-nucleotide polymorphism; Genome; ADME; Gene; Han chinese; Genotype","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263489,0.000358915,0.0004651925,0.001931573,0.0005434404,0.0006047293,0.0002940811,0.0002546415,0.001092164],"category_scores_gemma":[0.001346098,0.000113812,0.0003302462,0.003971568,0.0002223165,0.0002421322,0.0004859985,0.0002330505,0.0000862009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003243177,"about_ca_system_score_gemma":0.0003273161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01470176,"about_ca_topic_score_gemma":0.01957011,"domain_scores_codex":[0.9995185,0.0001103877,0.000064028,0.0001557428,0.00008648097,0.00006488687],"domain_scores_gemma":[0.999619,0.00008406804,0.0001229873,0.00004652997,0.00006770852,0.00005967972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005158856,0.0000356948,0.9347891,0.0001041044,0.0006904507,0.0007978969,0.001587686,0.000970149,0.01574635,0.0005403432,0.000413359,0.04380909],"study_design_scores_gemma":[0.00001615844,0.00002607055,0.9969925,0.0000126134,0.00009252523,0.000151615,0.0003306678,0.001109616,0.0002287223,0.0001323694,0.0009006186,0.00000656155],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977558,0.0002404553,0.0008935571,0.00002260628,0.000004404802,0.00001081867,0.0006790664,0.000008473572,0.0003847246],"genre_scores_gemma":[0.994993,0.0001901954,0.001964929,0.00002640678,0.000008220097,0.00001771842,0.002573307,0.00000647265,0.0002197479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01470176,"threshold_uncertainty_score":0.02923238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07352051497403943,"score_gpt":0.3339613201060064,"score_spread":0.2604408051319669,"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."}}