{"id":"W3215447310","doi":"10.1101/2021.11.29.470422","title":"<i>TractoInferno</i> : A large-scale, open-source, multi-site database for machine learning dMRI tractography","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute; Université de Montréal; Hôpital du Sacré-Cœur de Montréal; Université de Sherbrooke","funders":"","keywords":"Computer science; Tractography; Benchmarking; Artificial intelligence; Diffusion MRI; Database; Pipeline (software); Human Connectome Project; Artificial neural network; Data mining; Machine learning; Pattern recognition (psychology); Functional connectivity; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.002380307,0.002675892,0.001811789,0.003559484,0.0008979894,0.003036254,0.004571932,0.001873508,0.04125382],"category_scores_gemma":[0.008141506,0.001235721,0.001488359,0.003811855,0.0007935166,0.002976217,0.003706981,0.001748348,0.03720112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009999993,"about_ca_system_score_gemma":0.002017282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006270297,"about_ca_topic_score_gemma":0.01084597,"domain_scores_codex":[0.9987191,0.0001521271,0.0001431998,0.0004105567,0.0004477653,0.0001272305],"domain_scores_gemma":[0.9962019,0.0009356102,0.0003367107,0.001553508,0.0005428368,0.0004294344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00100332,0.0001595101,0.003397758,0.001729109,0.0003680321,0.0005147043,0.000218943,0.008095439,0.009156789,0.005183636,0.9037449,0.06642791],"study_design_scores_gemma":[0.001132428,0.0003577677,0.01480705,0.0005072437,0.0002105692,0.002052394,0.0001669614,0.07783411,0.03770374,0.01981497,0.844926,0.0004867098],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0139559,0.001483278,0.08917413,0.0004789257,0.0003416773,0.0005012607,0.5607907,0.324692,0.008582212],"genre_scores_gemma":[0.0325234,0.0004773401,0.06703231,0.0002581541,0.00009021816,0.0008915336,0.8627945,0.03210386,0.0038288],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9954281,"threshold_uncertainty_score":0.1380078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04702180029467767,"score_gpt":0.3130632676630124,"score_spread":0.2660414673683347,"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."}}