{"id":"W2036436660","doi":"10.1002/nbm.1586","title":"Considerations for measuring the fractional anisotropy of metabolites with diffusion tensor spectroscopy","year":2010,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anisotropy; Diffusion MRI; Fractional anisotropy; Nuclear magnetic resonance; Isotropy; Chemistry; Thermal diffusivity; Nuclear magnetic resonance spectroscopy; White matter; Spectroscopy; Diffusion; Attenuation; Analytical Chemistry (journal); Physics; Optics; Chromatography; Magnetic resonance imaging; Thermodynamics; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001461607,0.00008951496,0.0001938947,0.0001199496,0.00009289034,0.000004659088,0.00005291231,0.00003542978,0.00008190591],"category_scores_gemma":[0.0003122402,0.00005045877,0.00003272483,0.000222258,0.0002463133,0.00003622995,0.00001418835,0.0002217829,8.941013e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001512894,"about_ca_system_score_gemma":0.00007126818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002787271,"about_ca_topic_score_gemma":0.00002185168,"domain_scores_codex":[0.9993047,0.000008264019,0.0002120927,0.0001686103,0.0001777315,0.0001285937],"domain_scores_gemma":[0.9991352,0.000293117,0.00009034926,0.0002783722,0.0001472072,0.00005578762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008927578,0.0001450401,0.01561775,0.00002434037,0.00001084117,0.000002403641,0.00006126275,0.000002337438,0.9695464,0.01352126,0.0006589981,0.0003200353],"study_design_scores_gemma":[0.005828515,0.001066571,0.2658991,0.0002516728,0.0002783971,0.0004783643,0.0003099193,0.002199651,0.6214024,0.04017029,0.06184984,0.0002653328],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8761417,0.0001685881,0.08474673,0.03618856,0.0001132956,0.001666414,0.00004915336,0.0001296283,0.0007959811],"genre_scores_gemma":[0.8486829,0.00003960442,0.1502732,0.0006122934,0.0001698055,0.0001291085,0.00001644355,0.00001406249,0.00006257147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3481441,"threshold_uncertainty_score":0.2057649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05586982234251825,"score_gpt":0.3531460883994528,"score_spread":0.2972762660569346,"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."}}