{"id":"W2738800716","doi":"10.1002/hbm.23741","title":"Ax<scp>T</scp>ract: Toward microstructure informed tractography","year":2017,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire de Sherbrooke; Université de Sherbrooke","funders":"H2020 European Research Council; Horizon 2020; Natural Sciences and Engineering Research Council of Canada; Centre d'Imagerie BioMédicale","keywords":"Tractography; White matter; Diffusion MRI; Streamlines, streaklines, and pathlines; Computer science; Connectomics; Artificial intelligence; Magnetic resonance imaging; Neuroscience; Microstructure; Geology; Computer vision; Physics; Psychology; Connectome; Materials science; Medicine; Radiology; Functional connectivity","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":[0.001136364,0.0007662913,0.0004798575,0.00132108,0.000327494,0.001568253,0.0007906465,0.001052689,0.004808461],"category_scores_gemma":[0.003746444,0.000325034,0.0005735781,0.001189469,0.0006708886,0.001080249,0.001120084,0.0009397719,0.003008446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004265664,"about_ca_system_score_gemma":0.000969129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002675991,"about_ca_topic_score_gemma":0.003651109,"domain_scores_codex":[0.9996496,0.0001232525,0.00001924404,0.00006701441,0.0001209901,0.00001994123],"domain_scores_gemma":[0.9985702,0.0004846987,0.0002756759,0.000260564,0.0003101095,0.00009873006],"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":[0.0005319009,0.0001259472,0.003717512,0.0006721835,0.0003094562,0.001418423,0.0002698085,0.2646199,0.1045979,0.05341129,0.03778922,0.5325365],"study_design_scores_gemma":[0.00003131643,0.00008205018,0.001959014,0.000052624,0.00003072987,0.000524786,0.0000266383,0.9385415,0.02139798,0.02219378,0.01511536,0.00004423224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01009315,0.0003970645,0.9835976,0.0004277705,0.00005795415,0.0000485212,0.0004577471,0.003357046,0.00156313],"genre_scores_gemma":[0.09650321,0.001007941,0.8959575,0.0002891874,0.0001426383,0.0001560881,0.001089902,0.001330157,0.003523472],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004808461,"threshold_uncertainty_score":0.01608586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1100081508122313,"score_gpt":0.3775074931784151,"score_spread":0.2674993423661838,"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."}}