{"id":"W4388756314","doi":"10.1038/s41598-023-46972-6","title":"Author Correction: Predicting cognitive decline in a low-dimensional representation of brain morphology","year":2023,"lang":"en","type":"erratum","venue":"Scientific Reports","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre de Recherche Industrielle du Québec","funders":"","keywords":"Morphology (biology); Representation (politics); Cognition; Brain morphometry; Cognitive science; Computer science; Artificial intelligence; Neuroscience; Psychology; Biology; Zoology; Medicine; Political science; Magnetic resonance imaging; Radiology","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005861079,0.0003789407,0.0006419136,0.0005251188,0.0003493716,0.00007488432,0.000238713,0.000522179,0.002264475],"category_scores_gemma":[0.005693813,0.0004114073,0.0001760586,0.001991214,0.001135358,0.0003095949,0.0008452024,0.001124833,0.0004987586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000538389,"about_ca_system_score_gemma":0.0003119865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00190754,"about_ca_topic_score_gemma":0.001995236,"domain_scores_codex":[0.9927623,0.0005182386,0.00164981,0.002610207,0.001725524,0.0007338856],"domain_scores_gemma":[0.996416,0.0007194406,0.001599215,0.0009616302,0.00007506178,0.0002286458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00003842902,0.0002233867,0.1081812,0.00008839555,0.0000291257,0.00211855,0.0009107447,0.002491793,0.003183421,5.720638e-7,0.8770905,0.005643893],"study_design_scores_gemma":[0.001421907,0.0003397493,0.8245861,0.00430028,0.0002118525,0.001739146,0.002174894,0.03057507,0.008973032,0.005201251,0.1186475,0.001829247],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.7968182,0.00009904661,0.00042426,0.001558711,0.1808736,0.002155617,0.00006848441,0.0001967676,0.0178053],"genre_scores_gemma":[0.4883829,0.0000237503,0.0003308301,0.0005061325,0.0006377705,0.0002770437,0.003004786,0.0001565701,0.5066803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7584429,"threshold_uncertainty_score":0.9998338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02532534078708321,"score_gpt":0.3131186575705091,"score_spread":0.2877933167834259,"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."}}