{"id":"W2951290748","doi":"10.1016/j.neuroimage.2017.07.028","title":"Fiber tractography using machine learning","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Deutsche Forschungsgemeinschaft","keywords":"Tractography; Computer science; Artificial intelligence; Random forest; Diffusion MRI; Imaging phantom; Fiber; Pattern recognition (psychology); Machine learning; Chemistry; Medicine; Nuclear medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005692658,0.0001184024,0.0001546886,0.0000686363,0.0005137652,0.00007033753,0.0001628704,0.00003261604,0.0001244227],"category_scores_gemma":[0.0001218567,0.000110736,0.0001022225,0.00005984087,0.0001185196,0.0001553601,0.00008611598,0.0003812045,0.000037741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009465392,"about_ca_system_score_gemma":0.00001316381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004119507,"about_ca_topic_score_gemma":8.537373e-7,"domain_scores_codex":[0.9992691,0.00001495537,0.0001275111,0.0002785264,0.0001232389,0.0001866892],"domain_scores_gemma":[0.9989995,0.00002371958,0.0001273497,0.0007197311,0.0000393649,0.00009031991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001023878,0.0003378516,0.2257815,0.00008467885,0.00002753495,0.0005048243,0.00005252712,0.00004194848,0.7219416,0.0006963029,0.001128241,0.04930054],"study_design_scores_gemma":[0.001518367,0.0002976735,0.3667316,0.0001052076,0.0001714378,0.0007449884,0.000007145674,0.01549748,0.04516471,0.001089999,0.5682225,0.0004488012],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474502,0.0001255171,0.00759352,0.003645355,0.00009515308,0.0005758782,0.0000179706,0.000793209,0.03970326],"genre_scores_gemma":[0.9685892,0.00006226675,0.02866706,0.000510386,0.0001013975,0.00001122383,0.000008944183,0.00004870045,0.0020008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6767769,"threshold_uncertainty_score":0.4515685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1365906204835527,"score_gpt":0.3990254124329575,"score_spread":0.2624347919494048,"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."}}