{"id":"W4406270907","doi":"10.1007/s10334-026-01355-6","title":"Associations between iron and mean kurtosis in iron-rich grey matter nuclei in aging","year":2025,"lang":"en","type":"preprint","venue":"Magnetic Resonance Materials in Physics Biology and Medicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Kurtosis; Grey matter; Mathematics; Statistics; Medicine; Magnetic resonance imaging; White matter","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004322473,0.0002919507,0.0009842465,0.0002449744,0.00004454527,0.00001159944,0.0001390247,0.0002942074,0.00004141474],"category_scores_gemma":[0.00008977786,0.0002634936,0.00002078772,0.000243362,0.0003237092,0.00003012166,0.0003833942,0.0005455729,0.000001938896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008927388,"about_ca_system_score_gemma":0.00004296425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007654973,"about_ca_topic_score_gemma":0.00006291379,"domain_scores_codex":[0.9981024,0.0001507969,0.0006679153,0.0006689371,0.00008655914,0.0003234015],"domain_scores_gemma":[0.999123,0.0002145144,0.0001879878,0.0003862696,0.00003558499,0.00005265423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005411048,0.0001162354,0.9371262,0.0005409932,0.000006899974,0.00001609504,0.0009654267,0.000004069562,0.01551681,0.002082597,0.0003026565,0.04326788],"study_design_scores_gemma":[0.001433479,0.0001326565,0.9518795,0.001792317,0.00007255515,0.000003311032,0.000039367,0.00003204033,0.001670315,0.04162195,0.001092692,0.0002298218],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868993,0.003461072,0.0001233732,0.007211193,0.0001528081,0.000950625,0.0002458816,0.00004377577,0.0009119522],"genre_scores_gemma":[0.9902907,0.005469668,0.001994026,0.001048494,0.0003299165,0.0002434453,0.0003693672,0.00002433608,0.000230036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04303806,"threshold_uncertainty_score":0.9999817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03918465913785948,"score_gpt":0.3541619929633645,"score_spread":0.3149773338255051,"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."}}