{"id":"W2071833700","doi":"10.1155/2008/320195","title":"Accurate Anisotropic Fast Marching for Diffusion‐Based Geodesic Tractography","year":2007,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Montreal Neurological Institute and Hospital","funders":"Dr Hadwen Trust for Humane Research","keywords":"Fast marching method; Geodesic; Computer science; Robustness (evolution); Tractography; Diffusion MRI; Algorithm; Noisy data; Computation; Perturbation (astronomy); Anisotropy; Mathematics; Mathematical analysis; Physics","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":[0.001574664,0.001303295,0.00102501,0.001094124,0.000743175,0.0009187603,0.001083391,0.001429382,0.001382971],"category_scores_gemma":[0.008280744,0.0008045431,0.0008113837,0.001423728,0.0009683011,0.001407026,0.001175038,0.001743637,0.0006933166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008714022,"about_ca_system_score_gemma":0.001619233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006432352,"about_ca_topic_score_gemma":0.006047181,"domain_scores_codex":[0.9994955,0.0001836051,0.00003537965,0.00005919725,0.0002032728,0.00002299964],"domain_scores_gemma":[0.9981968,0.0009603065,0.0001758815,0.0003230609,0.0002817406,0.00006225199],"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":[0.00009211765,0.00002464687,0.0006785594,0.0001764605,0.00007602545,0.0002182227,0.0002122317,0.7984446,0.01646473,0.04463749,0.002020577,0.1369544],"study_design_scores_gemma":[0.00001196724,0.000008167961,0.00008091783,0.000004114024,0.000004431684,0.00003066293,0.000004654146,0.9863214,0.001867818,0.01030548,0.001351716,0.00000875363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007678906,0.0001809131,0.991082,0.0001557657,0.0000235348,0.00002817757,0.00005064099,0.0003736829,0.000426375],"genre_scores_gemma":[0.07760322,0.0002920942,0.9206619,0.00001970317,0.00002122348,0.0001192479,0.0001505777,0.0001965165,0.0009355609],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006432352,"threshold_uncertainty_score":0.01278985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04413663304776345,"score_gpt":0.4014732143271156,"score_spread":0.3573365812793521,"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."}}