{"id":"W2116129675","doi":"10.3389/fneur.2014.00216","title":"Beyond Crossing Fibers: Bootstrap Probabilistic Tractography Using Complex Subvoxel Fiber Geometries","year":2014,"lang":"en","type":"article","venue":"Frontiers in Neurology","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Montreal Neurological Institute and Hospital; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Tractography; Diffusion MRI; Fiber; Computer science; Probabilistic logic; Artificial intelligence; Voxel; Fiber tract; Human Connectome Project; Pipeline (software); Pattern recognition (psychology); Magnetic resonance imaging; Neuroscience; Psychology; Functional connectivity; Materials science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002613185,0.000571512,0.0006257175,0.001074118,0.0005093866,0.001076087,0.001023443,0.0009914723,0.0009013626],"category_scores_gemma":[0.01178224,0.0005384855,0.0007143445,0.0008926892,0.001334619,0.001758196,0.00159675,0.001063186,0.0004070082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005966858,"about_ca_system_score_gemma":0.0008689529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002753449,"about_ca_topic_score_gemma":0.003369427,"domain_scores_codex":[0.9993469,0.0002791954,0.00002681535,0.0001178006,0.0001904617,0.00003881184],"domain_scores_gemma":[0.9956989,0.002709541,0.0004985245,0.000644764,0.000317097,0.0001311695],"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":[0.0001781389,0.00003147239,0.003916391,0.000106441,0.00006958497,0.000353659,0.0003999202,0.8231001,0.01623164,0.0520191,0.0007664135,0.102827],"study_design_scores_gemma":[0.000003915604,0.00002167416,0.0005487466,0.000008645774,0.000004573294,0.00007992523,0.00001058779,0.9790172,0.001845893,0.01795014,0.0004972102,0.00001159858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01930599,0.00006704149,0.9799179,0.0000591154,0.000003518586,0.00001721518,0.00003909719,0.0002612768,0.0003287881],"genre_scores_gemma":[0.4124942,0.0002628793,0.5859648,0.00004068967,0.0000274987,0.00007921654,0.000250641,0.0002738289,0.0006062565],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002753449,"threshold_uncertainty_score":0.01381999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0797998667514669,"score_gpt":0.3461417527134482,"score_spread":0.2663418859619813,"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."}}