{"id":"W3172378891","doi":"10.31234/osf.io/468xa","title":"Using Diffusion Tensor Imaging to examine brain structural plasticity and language experience","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Diffusion MRI; Fractional anisotropy; Neuroimaging; White matter; Variation (astronomy); Computer science; Psychology; Neuroscience; Physics; Magnetic resonance imaging; 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":[0.001391325,0.0005328702,0.0002712831,0.002315858,0.0002825856,0.001129138,0.0002678079,0.0005188801,0.00139258],"category_scores_gemma":[0.00309114,0.0002426646,0.0003131957,0.00149467,0.0009627462,0.00139669,0.0006714042,0.000658033,0.0002423109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002718432,"about_ca_system_score_gemma":0.0005627365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002291571,"about_ca_topic_score_gemma":0.004234391,"domain_scores_codex":[0.9997032,0.00010569,0.0000321114,0.00006514516,0.00006262028,0.00003131959],"domain_scores_gemma":[0.9993041,0.0002610075,0.0002191879,0.00008725331,0.00005322788,0.00007520715],"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":[0.0006703651,0.0003237075,0.1442457,0.0008940212,0.0008178214,0.001854229,0.003032435,0.008765499,0.4446556,0.01852656,0.002270277,0.3739438],"study_design_scores_gemma":[0.0001529261,0.002104687,0.7405024,0.0002792268,0.0004293899,0.01060657,0.002617275,0.04340889,0.09511802,0.0886809,0.01582153,0.0002781212],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7718613,0.003411874,0.2142973,0.001338201,0.00009158213,0.000225022,0.001047015,0.0003068242,0.007421073],"genre_scores_gemma":[0.8698796,0.005036248,0.1210287,0.0002313713,0.0001271353,0.0003168004,0.0005078741,0.0001177786,0.002754376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002315858,"threshold_uncertainty_score":0.007358074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08196933927449837,"score_gpt":0.4069727400210898,"score_spread":0.3250034007465914,"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."}}