{"id":"W3006287032","doi":"10.1002/nbm.4270","title":"Rapid acquisition diffusion MR spectroscopy of metabolites in human brain","year":2020,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Diffusion MRI; Diffusion; Data acquisition; White matter; Nuclear magnetic resonance; Diffusion imaging; Spectroscopy; Computer science; Physics; Biological system; Magnetic resonance imaging; Medicine; Biology","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":[0.0008320787,0.0004093557,0.0002934671,0.0005319879,0.0001836733,0.0003619201,0.0002729428,0.0005365527,0.001441445],"category_scores_gemma":[0.001195111,0.0002518148,0.0001403876,0.0003360563,0.0002890393,0.0008614316,0.0003893792,0.0003593632,0.0005649781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000114306,"about_ca_system_score_gemma":0.0003102199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002985385,"about_ca_topic_score_gemma":0.0005612634,"domain_scores_codex":[0.9998342,0.00005555921,0.000009630171,0.00005143603,0.00003699319,0.00001216321],"domain_scores_gemma":[0.9998105,0.0000677067,0.00003643235,0.00002345762,0.00004377364,0.00001812628],"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":[0.0004320949,0.00005991982,0.0006920359,0.0005304617,0.00004124907,0.0002038134,0.00009048598,0.0006761161,0.9332789,0.001156413,0.001099594,0.06173889],"study_design_scores_gemma":[0.0003014635,0.002638152,0.0207629,0.0001411261,0.0002001306,0.007534797,0.0001825443,0.01215061,0.9104577,0.007006896,0.03846318,0.0001604704],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5662398,0.04680705,0.3745742,0.00174499,0.0003227221,0.0005417955,0.001093962,0.001158874,0.007516739],"genre_scores_gemma":[0.714674,0.02130194,0.2571717,0.0004846365,0.0002706368,0.0004309307,0.000974854,0.0002358209,0.004455455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001441445,"threshold_uncertainty_score":0.004822135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0527022688788116,"score_gpt":0.3695152712650518,"score_spread":0.3168130023862402,"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."}}