{"id":"W4411894088","doi":"10.1002/mrm.30620","title":"Myelin water and tensor‐valued diffusion imaging: (How) are they related?","year":2025,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Collaboration On Repair Discoveries; Philips (Canada); University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Multiple Sclerosis Society of Canada; Michael Smith Health Research BC","keywords":"Diffusion MRI; Fractional anisotropy; White matter; Myelin; Pathological; Tractography; Pathology; Abnormality; Correlation; Multiple sclerosis; Medicine; Neuroscience; Biology; Magnetic resonance imaging; Central nervous system; Radiology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002758332,0.0005804761,0.0007686415,0.002041546,0.0003365398,0.002240835,0.0005935681,0.0008931673,0.00190111],"category_scores_gemma":[0.01883065,0.0004217503,0.0005488754,0.002312396,0.001767201,0.003513213,0.001005804,0.0009683846,0.0004522361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006067834,"about_ca_system_score_gemma":0.0007204284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004616723,"about_ca_topic_score_gemma":0.002934116,"domain_scores_codex":[0.9988384,0.0003957193,0.00009275066,0.0002812514,0.0002481249,0.0001437481],"domain_scores_gemma":[0.992269,0.002124947,0.003021917,0.0005974305,0.001622766,0.0003639707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004929593,0.0001475519,0.7671131,0.0007637701,0.0011281,0.0004746385,0.001918286,0.001503421,0.01011145,0.005790545,0.001760907,0.2087953],"study_design_scores_gemma":[0.00001890278,0.0003407445,0.973193,0.0003354465,0.0003280707,0.001481763,0.001576278,0.004436792,0.002150222,0.01377226,0.002286347,0.00008010459],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9272346,0.0465733,0.01477356,0.004714897,0.0002857723,0.00008911192,0.000470567,0.0001314211,0.005726828],"genre_scores_gemma":[0.9896753,0.00527045,0.003935417,0.0002631473,0.0001800097,0.00002616934,0.0001242878,0.00002827805,0.0004967568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004616723,"threshold_uncertainty_score":0.01458764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02490476242195573,"score_gpt":0.3171838362803847,"score_spread":0.292279073858429,"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."}}