{"id":"W4282933907","doi":"10.3389/fninf.2022.883223","title":"A Robust Modular Automated Neuroimaging Pipeline for Model Inputs to TheVirtualBrain","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Baycrest Hospital; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Medical Research Council; Compute Canada; BrightFocus Foundation","keywords":"Computer science; Biobank; Pipeline (software); Neuroimaging; Modalities; Connectome; Modular design; Leverage (statistics); Data science; Artificial intelligence; Scalability; Machine learning; Data mining; Database; Bioinformatics; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005710705,0.0002786451,0.0003669203,0.0006009678,0.0006137243,0.00008322327,0.0006161098,0.00003124998,0.000005486576],"category_scores_gemma":[0.007624205,0.000309893,0.0001100666,0.0009958999,0.00007355428,0.0004934787,0.0006839309,0.0004073727,0.000009459905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002270118,"about_ca_system_score_gemma":0.0001081967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002651883,"about_ca_topic_score_gemma":0.000001999816,"domain_scores_codex":[0.9975947,0.0001481645,0.0005879388,0.0004847838,0.0006004005,0.0005840397],"domain_scores_gemma":[0.9980301,0.001151412,0.0001688881,0.0004843978,0.00005046834,0.0001147273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001062423,0.00007507775,0.0001107287,0.00003722649,0.000003550997,0.00001148024,0.001292616,0.8320714,0.002064465,0.0004389421,0.1627216,0.001066677],"study_design_scores_gemma":[0.000776232,0.0001560959,0.00004885707,0.000009518762,0.000008738516,0.00002421573,0.0003325242,0.9748383,0.001008555,0.001202629,0.02129908,0.0002952928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09903757,0.00004503213,0.8708019,0.01973999,0.003715295,0.002791463,0.0004973829,0.001266686,0.002104692],"genre_scores_gemma":[0.8030937,0.00002973473,0.09516683,0.09881226,0.0001184886,0.001029697,0.00003547882,0.0001647163,0.001549086],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7756351,"threshold_uncertainty_score":0.9999353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04869408097994354,"score_gpt":0.2589611488286493,"score_spread":0.2102670678487058,"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."}}