{"id":"W2887072805","doi":"10.1016/j.neuroimage.2018.10.029","title":"Limits to anatomical accuracy of diffusion tractography using modern approaches","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":282,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"National Center for Research Resources; National Institute of Neurological Disorders and Stroke; Vanderbilt Institute for Clinical and Translational Research; National Institute on Aging; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; Vanderbilt University","keywords":"Tractography; Diffusion MRI; Computer science; White matter; Artificial intelligence; Neuroscience; Psychology; Magnetic resonance imaging; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00005339597,0.0001318431,0.000213736,0.0001712697,0.00007417754,0.00001118782,0.0001368974,0.00004505631,0.00001483837],"category_scores_gemma":[0.000136425,0.000119563,0.00009602694,0.0004123941,0.0001465488,0.00008706465,0.00007604634,0.0001691277,0.000007471078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001270119,"about_ca_system_score_gemma":0.00002392961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008807201,"about_ca_topic_score_gemma":6.83341e-7,"domain_scores_codex":[0.9990248,0.00002032796,0.0002267551,0.0003540787,0.0001761875,0.0001978485],"domain_scores_gemma":[0.9991136,0.00006471606,0.00008725278,0.0005124595,0.00008267909,0.0001393141],"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":[0.00009555801,0.0002856322,0.004900327,0.00002997819,0.000005133309,0.000009087003,0.0001152258,0.000004812015,0.9686915,0.0003720115,0.0003254234,0.02516531],"study_design_scores_gemma":[0.001426098,0.001136143,0.1645446,0.0001976194,0.0001771782,0.0002575458,0.0000484904,0.06768314,0.745492,0.003857429,0.01467182,0.0005079485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9218807,0.00001789227,0.07512215,0.0008640106,0.00002914396,0.0004620169,0.00001143224,0.0001610322,0.001451671],"genre_scores_gemma":[0.9375687,0.00001101439,0.06148089,0.0007575267,0.0001005213,0.00001376916,0.000004763668,0.00003465374,0.00002813409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2231995,"threshold_uncertainty_score":0.4875639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.220788248719264,"score_gpt":0.3910391396846636,"score_spread":0.1702508909653996,"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."}}