{"id":"W4391325391","doi":"10.1002/cpz1.972","title":"Recovering Misidentified Samples Through Genetic Discordance Clustering","year":2024,"lang":"en","type":"article","venue":"Current Protocols","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Multiple Sclerosis Society; Multiple Sclerosis Society of Canada; European Genomic Institute for Diabetes","keywords":"Genotyping; Sample (material); Protocol (science); Computer science; Quality assurance; Guideline; Data science; Medicine; Biology; Pathology; Genetics; Genotype; External quality assessment; Alternative medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0228177,0.0020954,0.002070776,0.007310951,0.003858364,0.004830554,0.004861303,0.002565541,0.004244913],"category_scores_gemma":[0.05544897,0.001657311,0.001950865,0.004153036,0.00207204,0.001724968,0.007488587,0.003421036,0.006558832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001551781,"about_ca_system_score_gemma":0.003591936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002989349,"about_ca_topic_score_gemma":0.006591043,"domain_scores_codex":[0.9673811,0.008803371,0.003541979,0.007300183,0.0116259,0.001347484],"domain_scores_gemma":[0.9626691,0.009230942,0.004629789,0.01329904,0.009475955,0.0006950604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001359003,0.0004811871,0.07962731,0.002561204,0.00105091,0.004308193,0.009527951,0.009398981,0.2187172,0.03502245,0.04478501,0.5931607],"study_design_scores_gemma":[0.0001658917,0.0004861538,0.0597153,0.001670439,0.0008217474,0.01089945,0.002788201,0.09364159,0.5086477,0.07806423,0.2423254,0.00077402],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06168032,0.001824441,0.9173355,0.001176857,0.001240592,0.001607536,0.00303667,0.004760953,0.007337258],"genre_scores_gemma":[0.1228058,0.0009797555,0.8573446,0.0010471,0.0001857223,0.001271569,0.006376553,0.001746039,0.008242851],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0228177,"threshold_uncertainty_score":0.120673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07448014551770323,"score_gpt":0.3835014261622324,"score_spread":0.3090212806445292,"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."}}