{"id":"W3163340744","doi":"10.71781/18169","title":"Assessing early white matter predictors of syntactic abilities in post-stroke aphasia using HARDI-based tractography","year":2020,"lang":"en","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Réseau québécois de recherche sur le vieillissement; Heart and Stroke Foundation of Canada","keywords":"Aphasia; Tractography; White matter; Diffusion MRI; Stroke (engine); Psychology; Medicine; Neuroscience; Magnetic resonance imaging; Engineering; Radiology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000973643,0.000555955,0.000347585,0.00123742,0.0002460655,0.0009256846,0.0002672244,0.0006206574,0.002407341],"category_scores_gemma":[0.002928566,0.0002592356,0.0004228042,0.0007388642,0.000410904,0.0007244528,0.0003866951,0.0003460075,0.0004226962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004127847,"about_ca_system_score_gemma":0.0007357478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01156102,"about_ca_topic_score_gemma":0.025508,"domain_scores_codex":[0.9998072,0.00005493521,0.00001888029,0.00004734217,0.00003376887,0.00003801094],"domain_scores_gemma":[0.9991295,0.0004275516,0.0001788702,0.00008235875,0.0001093613,0.00007241662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001656162,0.0001805065,0.8026392,0.0003775427,0.0007900695,0.001104489,0.001030842,0.007560679,0.08289157,0.0009730331,0.0007018019,0.1000941],"study_design_scores_gemma":[0.0000247183,0.0002502173,0.9730282,0.0000512626,0.0001438473,0.0007696129,0.0003555113,0.01567484,0.007775725,0.00129735,0.0005930898,0.00003555733],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894925,0.000593061,0.008384396,0.00008796111,0.000008757187,0.00005230584,0.0003975638,0.00007267864,0.0009107615],"genre_scores_gemma":[0.993679,0.000395656,0.004384254,0.00001883675,0.000009453339,0.00006234517,0.0003244513,0.00002001207,0.001106027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01156102,"threshold_uncertainty_score":0.02298748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01122869628921376,"score_gpt":0.2130149156966433,"score_spread":0.2017862194074296,"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."}}