{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007272339,0.0009230874,0.0008544474,0.00116574,0.0004248241,0.001081115,0.0009516123,0.001294694,0.004699831],"category_scores_gemma":[0.002925081,0.0005759894,0.001270125,0.001238557,0.0007731278,0.001021966,0.001103163,0.001126203,0.001350055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00135712,"about_ca_system_score_gemma":0.001719783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008889503,"about_ca_topic_score_gemma":0.01299999,"domain_scores_codex":[0.9994442,0.0001631561,0.00002849578,0.0001389467,0.0001690345,0.00005611422],"domain_scores_gemma":[0.9986497,0.0006917942,0.0002139341,0.0001993524,0.0001868329,0.00005847998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008733474,0.00002405795,0.001108808,0.000136866,0.00009098167,0.0001525596,0.00008698653,0.8791763,0.006225022,0.02716471,0.003534282,0.08221205],"study_design_scores_gemma":[0.00000521314,0.000009501464,0.0001823422,0.000006201571,0.000006997674,0.00002933327,0.000003298568,0.9904307,0.000621799,0.007640167,0.001059821,0.000004674415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005223432,0.0001988741,0.9922198,0.0001399729,0.00001678376,0.00003349056,0.0002424256,0.000559559,0.001365561],"genre_scores_gemma":[0.3536195,0.0006936513,0.6361464,0.0002439312,0.0001110933,0.0002824063,0.001906833,0.0004603428,0.006535855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008889503,"threshold_uncertainty_score":0.01767552,"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."}}