{"id":"W2911926566","doi":"10.1016/j.mri.2019.04.013","title":"Tractography and machine learning: Current state and open challenges","year":2019,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":85,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Tractography; Machine learning; False positive paradox; Prior probability; Bayesian probability; Diffusion MRI","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":[0.03898402,0.001328321,0.0050503,0.003735005,0.001324534,0.01329256,0.00523236,0.007528246,0.009291951],"category_scores_gemma":[0.05107504,0.001097421,0.001301772,0.004687287,0.01313,0.02264394,0.005138987,0.007389305,0.003230775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002711994,"about_ca_system_score_gemma":0.008352799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004712855,"about_ca_topic_score_gemma":0.006064339,"domain_scores_codex":[0.9932059,0.003256849,0.0007021077,0.001203375,0.001294763,0.0003369891],"domain_scores_gemma":[0.8242076,0.1503856,0.004434388,0.004799174,0.01336617,0.002807077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005697773,0.0003512148,0.006950221,0.006249573,0.0003828072,0.0001315601,0.0005244971,0.003966798,0.0008548012,0.0893177,0.02706106,0.86364],"study_design_scores_gemma":[0.0001575259,0.0005429948,0.005784729,0.01177913,0.0003106204,0.0008082054,0.00228924,0.03103199,0.001360734,0.6527959,0.2927412,0.0003977686],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003560505,0.8737672,0.04303421,0.07346676,0.001595855,0.00005846535,0.0002272404,0.0002126343,0.004077081],"genre_scores_gemma":[0.05446558,0.8591003,0.0601847,0.008043882,0.01537614,0.0002122861,0.0004858517,0.0001585289,0.00197269],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.03898402,"threshold_uncertainty_score":0.2061697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06153289244575866,"score_gpt":0.3413780662795929,"score_spread":0.2798451738338343,"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."}}