{"id":"W3024808442","doi":"10.1002/hbm.25028","title":"An automated machine learning approach to predict brain age from cortical anatomical measures","year":2020,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; National Institutes of Health; EPSRC Centre for Doctoral Training in Medical Imaging; National Institute on Aging; King's College London; Imperial College London; Age UK; Medical Research Council Canada; Wellcome Trust","keywords":"Hyperparameter; Artificial intelligence; Machine learning; Computer science; Neuroimaging; Pipeline (software); Relevance (law); Genetic programming; Biology; Neuroscience","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006152239,0.0003450943,0.0004602029,0.0001657424,0.00102326,0.0002266585,0.0005512169,0.0001213926,0.00008641208],"category_scores_gemma":[0.02458641,0.0003552451,0.0001165587,0.0006302284,0.0002248989,0.0002641325,0.0003298365,0.0007911784,0.0001184091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001007209,"about_ca_system_score_gemma":0.00003995094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009235925,"about_ca_topic_score_gemma":0.00002800178,"domain_scores_codex":[0.9959272,0.001050562,0.0004209148,0.001324717,0.0007173538,0.0005592355],"domain_scores_gemma":[0.995204,0.00383856,0.0001007599,0.0003494152,0.00004723552,0.0004600712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007742111,0.0001554944,0.003497727,0.00002797646,0.00003812155,0.00007723016,0.004383896,0.003075765,0.9645883,0.002930346,0.0208687,0.0002790351],"study_design_scores_gemma":[0.002008009,0.0007923847,0.2044649,0.00008713885,0.00003501288,0.00002587753,0.001108594,0.7311664,0.006239733,0.00130175,0.05155247,0.001217725],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9430002,0.00003752168,0.02286929,0.02585942,0.0001623571,0.0006674456,0.00007014751,0.003339338,0.003994287],"genre_scores_gemma":[0.9649671,7.513649e-7,0.001167523,0.03313336,0.0004428099,0.00004988342,0.00005955594,0.00006328663,0.0001157094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9583486,"threshold_uncertainty_score":0.99989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08850216220217058,"score_gpt":0.2968902308635825,"score_spread":0.2083880686614119,"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."}}