{"id":"W4362603986","doi":"10.1117/12.2653884","title":"Mapping the impact of approximate gradient nonlinearity fields correction on tractography","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of General Medical Sciences; National Institutes of Health","keywords":"Tractography; Diffusion MRI; Connectomics; Nonlinear system; Population; Voxel; Computer science; Statistical physics; Connectome; Artificial intelligence; Physics; Functional connectivity","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":[0.004562799,0.0007441303,0.0005589597,0.0007312504,0.000528692,0.001378039,0.0007042449,0.000582265,0.002227506],"category_scores_gemma":[0.03248346,0.0003023948,0.000645168,0.0009388632,0.0008073978,0.001275784,0.0008200246,0.0007880286,0.0008569815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005494619,"about_ca_system_score_gemma":0.001508261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004865,"about_ca_topic_score_gemma":0.00797892,"domain_scores_codex":[0.9981552,0.0008358031,0.0001324338,0.0003974084,0.0003858765,0.00009323315],"domain_scores_gemma":[0.9911142,0.005116306,0.0008714799,0.001722375,0.001058598,0.0001170132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001592071,0.000126435,0.08935648,0.0008591897,0.0008480048,0.0008652738,0.001396849,0.192811,0.1154432,0.01825272,0.005576022,0.5728727],"study_design_scores_gemma":[0.0001038825,0.0009450754,0.1669055,0.0002360292,0.0004423999,0.003413912,0.0005204465,0.6628553,0.1143038,0.02923972,0.02081768,0.0002162735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.248341,0.000967088,0.7451867,0.0004759945,0.000190956,0.000140191,0.0007730589,0.002262859,0.001662217],"genre_scores_gemma":[0.7330152,0.00042034,0.2623285,0.0001559725,0.00004408532,0.0001374002,0.00109181,0.001218055,0.00158868],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004865,"threshold_uncertainty_score":0.02413064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1186722705274807,"score_gpt":0.3889556291316619,"score_spread":0.2702833586041812,"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."}}