{"id":"W4404914054","doi":"10.1371/journal.pcbi.1012617","title":"Mapping genes for human face shape: Exploration of univariate phenotyping strategies","year":2024,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Cardiff University; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Medical Research Council; Vlaamse regering; Onderzoeksraad, KU Leuven; KU Leuven; National Institute of Dental and Craniofacial Research; University of Pennsylvania; Fonds Wetenschappelijk Onderzoek; University of Bristol; Wellcome Trust; Pennsylvania State University","keywords":"Landmark; Univariate; Genome-wide association study; Quantitative trait locus; Craniofacial; Artificial intelligence; Heritability; Computer science; Principal component analysis; Bivariate analysis; Biology; Evolutionary biology; Computational biology; Genetics; Multivariate statistics; Machine learning; Gene; Single-nucleotide polymorphism","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":[0.002911106,0.0009209539,0.0006400631,0.001482539,0.0003908916,0.00100167,0.0007305817,0.0005269464,0.001808314],"category_scores_gemma":[0.006303686,0.0003481484,0.001307912,0.001629575,0.000877345,0.0007386869,0.001005975,0.001012274,0.0001835695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003178935,"about_ca_system_score_gemma":0.0003516006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001890418,"about_ca_topic_score_gemma":0.002186734,"domain_scores_codex":[0.9984822,0.0006902505,0.00005022627,0.0004725833,0.0002410661,0.00006376216],"domain_scores_gemma":[0.9933389,0.004823383,0.0007531098,0.0007190487,0.0001819452,0.0001835864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005134975,0.0002206776,0.597875,0.0003295598,0.001660834,0.001610876,0.001437733,0.01949938,0.1181588,0.01924343,0.0007764888,0.2386738],"study_design_scores_gemma":[0.00005298724,0.000295732,0.8501971,0.0001279963,0.0008166948,0.001592526,0.0005989124,0.101256,0.01151253,0.03109318,0.002336361,0.0001200112],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8418388,0.002326607,0.1523558,0.0007950003,0.00002262541,0.00003649205,0.0008516454,0.0001830614,0.001590032],"genre_scores_gemma":[0.963055,0.0008195151,0.03520645,0.00009922183,0.00003069171,0.00005539934,0.0002478494,0.00006513944,0.0004206336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002911106,"threshold_uncertainty_score":0.01539558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1736587078535333,"score_gpt":0.3637948831304499,"score_spread":0.1901361752769166,"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."}}