{"id":"W4364352504","doi":"10.1111/1556-4029.15251","title":"Commentary on: Elkins <scp>KM</scp>, Garloff <scp>AT</scp>, Zeller <scp>CB</scp>. Additional predictions for forensic <scp>DNA</scp> phenotyping of externally visible characteristics using the <scp>ForenSeq</scp> and Imagen kits. J Forensic Sci. 2023;68(2):608–13. https://doi.org/10.1111/1556‐4029.15215","year":2023,"lang":"en","type":"letter","venue":"Journal of Forensic Sciences","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Pleiotropy; Computational biology; Genotyping; Genetics; Biology; Computer science; Phenotype; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.003860453,0.002032685,0.002477815,0.001675498,0.00269223,0.000877762,0.003082844,0.001744219,0.0001495692],"category_scores_gemma":[0.008260559,0.001633355,0.001800022,0.001926714,0.006548492,0.0002510506,0.00171322,0.003306217,0.0001413477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004439488,"about_ca_system_score_gemma":0.001972628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001877246,"about_ca_topic_score_gemma":0.0001561835,"domain_scores_codex":[0.9857624,0.000709886,0.003355235,0.002453078,0.004294423,0.003424987],"domain_scores_gemma":[0.9828194,0.007571352,0.004115904,0.001704022,0.002801171,0.0009881067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003024498,0.0002074514,0.001270421,0.0003982418,0.001271369,0.0002314302,0.0006907252,0.0006534058,0.01604239,0.00007730963,0.9768836,0.002243358],"study_design_scores_gemma":[0.003611533,0.004844428,0.007781646,0.002168409,0.001411948,0.001123062,0.004522139,0.006067722,0.04287231,0.004191514,0.9210606,0.0003446539],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.9452418,0.007658405,0.00439177,0.01266163,0.008765556,0.003988145,0.009128657,0.0001663047,0.007997713],"genre_scores_gemma":[0.1969577,0.02781662,0.06816345,0.2434005,0.1538173,0.002100575,0.03917111,0.004205645,0.2643672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7482842,"threshold_uncertainty_score":0.9995517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03526082081451971,"score_gpt":0.2931113532840495,"score_spread":0.2578505324695298,"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."}}