{"id":"W4416257142","doi":"10.1002/hbm.70408","title":"Utility of Harmonisation for Fixel‐Based Metrics in Travelling Subjects and Alzheimer's Disease Data","year":2025,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Japan Agency for Medical Research and Development; 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":"Diffusion MRI; Metric (unit); Comparability; Scanner; White matter; Subject 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":[],"consensus_categories":[],"category_scores_codex":[0.000297694,0.00007157306,0.0001478163,0.0002333947,0.00008606857,0.000009378233,0.0001031543,0.00002453712,0.000003356136],"category_scores_gemma":[0.000312417,0.00007751025,0.00002432666,0.0003160805,0.00005582108,0.00005443223,0.00005334221,0.00006524612,7.847505e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009658902,"about_ca_system_score_gemma":0.00005365007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001165388,"about_ca_topic_score_gemma":0.000005042989,"domain_scores_codex":[0.9993149,0.00001977763,0.0002167622,0.0002771545,0.00006721068,0.0001041748],"domain_scores_gemma":[0.9991276,0.0002851182,0.00006597488,0.000440569,0.000040168,0.00004062518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007760247,0.001987827,0.4264637,0.005449234,0.000220979,0.00003046829,0.0008818723,0.0001571126,0.2442264,0.1035964,0.01556211,0.2006479],"study_design_scores_gemma":[0.001615093,0.00003256115,0.7943083,0.0004576234,0.0001298089,5.145035e-7,0.00006701278,0.1619859,0.003670312,0.01838871,0.01920537,0.0001387494],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1570704,0.0007862011,0.8347646,0.00517904,0.00001964638,0.001504769,0.00009118744,0.0001126168,0.0004715155],"genre_scores_gemma":[0.9772405,0.00001219049,0.02185149,0.0006079563,0.00001338143,0.00003981163,0.0001916634,0.000008343152,0.00003467633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8201701,"threshold_uncertainty_score":0.3160776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3215893071424486,"score_gpt":0.4295181423094712,"score_spread":0.1079288351670226,"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."}}