{"id":"W3124084750","doi":"10.1089/brain.2020.0907","title":"Hierarchical Microstructure Informed Tractography","year":2021,"lang":"en","type":"article","venue":"Brain Connectivity","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"McDonnell Center for Systems Neuroscience; National Institutes of Health","keywords":"Tractography; Diffusion MRI; White matter; Computer science; Artificial intelligence; Magnetic resonance imaging; Radiomics; Pattern recognition (psychology); Radiology; Medicine","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":[0.00005826398,0.0001015634,0.0001726191,0.0000527612,0.00008899255,0.00001713154,0.00005153717,0.00006799492,0.00008439704],"category_scores_gemma":[0.0006148474,0.00009466113,0.0001165992,0.000335324,0.00009023105,0.00006933408,0.00004576442,0.000315606,0.000006409361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002346455,"about_ca_system_score_gemma":0.0001419788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004093571,"about_ca_topic_score_gemma":0.0000125243,"domain_scores_codex":[0.9993124,0.00002721057,0.0001186426,0.0002618198,0.0001059344,0.0001739699],"domain_scores_gemma":[0.9990493,0.0003643359,0.00003583295,0.0003684059,0.00007579807,0.0001063509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002330514,0.000776773,0.06844871,0.0002224361,0.0001055079,0.000624152,0.0003596213,0.000010867,0.7158877,0.05775061,0.04349138,0.1120892],"study_design_scores_gemma":[0.001197866,0.0001110645,0.4114922,0.00005556331,0.00003613829,0.001560102,0.00004103507,0.0001604977,0.09778818,0.02511449,0.4621831,0.0002597381],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9161723,0.0000981815,0.03903769,0.03667761,0.00006464814,0.0003482481,0.0000366945,0.0004370048,0.007127566],"genre_scores_gemma":[0.9705057,0.00002153967,0.02236226,0.006641434,0.00007720304,0.00003016076,0.00003931985,0.00001611169,0.0003062337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6180995,"threshold_uncertainty_score":0.3860169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04658254528705097,"score_gpt":0.3559203308303244,"score_spread":0.3093377855432735,"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."}}