{"id":"W4386549227","doi":"10.1162/imag_a_00019","title":"Dual-encoded magnetization transfer and diffusion imaging and its application to tract-specific microstructure mapping","year":2023,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Calgary; McGill University Health Centre; Montreal Neurological Institute and Hospital","funders":"Fonds de Recherche du Québec - Santé; Canada First Research Excellence Fund; Réseau en Bio-Imagerie du Quebec; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Magnetization transfer; White matter; Tractography; Diffusion MRI; Voxel; Partial volume; Fractional anisotropy; Nuclear magnetic resonance; Nuclear medicine; Biomedical engineering; Materials science; Computer science; Artificial intelligence; Magnetic resonance imaging; Physics; Medicine; Radiology","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":[0.0001256111,0.0001658258,0.0001461248,0.0002664151,0.0003188511,0.00009561507,0.00009893994,0.00002106729,0.000002644951],"category_scores_gemma":[0.00005644329,0.0001632032,0.00002305393,0.0009837651,0.0001251163,0.0002509978,0.0001000522,0.0001711286,0.000007876016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002085731,"about_ca_system_score_gemma":0.00001523728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003992363,"about_ca_topic_score_gemma":2.323492e-7,"domain_scores_codex":[0.9985742,0.00001780109,0.000204712,0.0006973932,0.0002095482,0.0002963324],"domain_scores_gemma":[0.9994269,0.00003912642,0.0000351662,0.0002648438,0.00006650343,0.0001674756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000006470008,0.00001689242,0.01008039,0.00002711593,1.702771e-7,0.00001763996,0.0002177016,0.0000196055,0.9638311,0.0003668203,0.0002526935,0.02516336],"study_design_scores_gemma":[0.0008799771,0.00005606169,0.7374213,0.0001370889,0.00002147807,0.001067137,0.0001501558,0.1269501,0.05730049,0.001103092,0.07446746,0.0004456468],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8394912,0.000248549,0.144299,0.01420022,0.00009748973,0.0009103384,0.00001929499,0.0006279026,0.0001059955],"genre_scores_gemma":[0.9947962,0.0005493421,0.001979698,0.002352004,0.00005740026,0.00007446775,0.00001377835,0.0000321832,0.0001449415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9065307,"threshold_uncertainty_score":0.6655232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03278757944572808,"score_gpt":0.3107966841154105,"score_spread":0.2780091046696824,"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."}}