{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02412028,0.0009048308,0.0009139712,0.002383533,0.001022221,0.00217749,0.001427845,0.0008717565,0.001588637],"category_scores_gemma":[0.06828556,0.0003367685,0.001184684,0.002569039,0.001431602,0.001470512,0.003140178,0.0008340903,0.0008657446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006604786,"about_ca_system_score_gemma":0.001118162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003642642,"about_ca_topic_score_gemma":0.004160904,"domain_scores_codex":[0.9879249,0.006624408,0.00107596,0.002861841,0.001238762,0.0002740833],"domain_scores_gemma":[0.9806978,0.007534868,0.00223794,0.00612813,0.003138003,0.0002632846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005123591,0.0006104114,0.1306764,0.001611411,0.003744944,0.0007353182,0.007215516,0.05323217,0.04083133,0.009101494,0.03083426,0.7162831],"study_design_scores_gemma":[0.001075253,0.00366819,0.4034421,0.0007154197,0.001796054,0.002390675,0.006092218,0.3697153,0.05759352,0.03731169,0.1155422,0.0006573405],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6151431,0.001730929,0.3563737,0.0007897636,0.0004181991,0.001762985,0.007726947,0.01132698,0.004727523],"genre_scores_gemma":[0.7155976,0.0002374734,0.2689568,0.0002144997,0.00009505945,0.001350151,0.01081304,0.001701554,0.00103392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02412028,"threshold_uncertainty_score":0.1275618,"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."}}