{"id":"W4406250231","doi":"10.1016/b978-0-12-818894-1.00021-5","title":"Improving tractography using anatomical priors and multimodal integration","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; Université du Québec en Outaouais","funders":"","keywords":"Prior probability; Tractography; Computer science; Artificial intelligence; Diffusion MRI; Medicine; Radiology; Bayesian probability; Magnetic resonance imaging","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":[0.0007143068,0.001685264,0.0008575721,0.001265676,0.0002302148,0.002030834,0.0009314061,0.001547348,0.01838953],"category_scores_gemma":[0.002351936,0.0008288836,0.0008588036,0.001680665,0.0006029348,0.001891682,0.0012102,0.002017373,0.01214605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000370534,"about_ca_system_score_gemma":0.0004933592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001983718,"about_ca_topic_score_gemma":0.00351477,"domain_scores_codex":[0.9997619,0.00003719952,0.00001278269,0.00007173733,0.0001047855,0.00001163362],"domain_scores_gemma":[0.9993555,0.0003532487,0.00004533771,0.0001117638,0.0001063944,0.00002768966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004276394,0.00002465596,0.0001267255,0.0002310201,0.00006090572,0.00009246591,0.00004149617,0.05310489,0.02066554,0.01995758,0.02197001,0.883682],"study_design_scores_gemma":[0.00002203657,0.0001161759,0.001294393,0.0002492256,0.0001195391,0.001268927,0.00004362305,0.692463,0.0311038,0.1339458,0.1392708,0.0001027184],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009586799,0.00202092,0.9891393,0.0002368607,0.0001767921,0.000008933957,0.0001059489,0.001778995,0.005573536],"genre_scores_gemma":[0.0157167,0.005636991,0.9487351,0.0001218153,0.000361656,0.00003249888,0.000496301,0.001229033,0.02766997],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01838953,"threshold_uncertainty_score":0.06151909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03340598621204195,"score_gpt":0.3243040757315122,"score_spread":0.2908980895194702,"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."}}