{"id":"W6931024577","doi":"10.5281/zenodo.2597526","title":"fMRIPrep: a robust preprocessing pipeline for functional MRI","year":2019,"lang":"en","type":"other","venue":"Figshare","topic":"Immune Response and Inflammation","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Pipeline (software); Preprocessor; Workflow; Pattern recognition (psychology); Pipeline transport","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":[],"consensus_categories":[],"category_scores_codex":[0.002684372,0.003075544,0.001381479,0.002876204,0.001484909,0.003438855,0.003863646,0.002439205,0.1300281],"category_scores_gemma":[0.007891051,0.002170352,0.001965149,0.001695203,0.0007185843,0.001868245,0.00350748,0.003299543,0.1014592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006589188,"about_ca_system_score_gemma":0.002000785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001979935,"about_ca_topic_score_gemma":0.004954785,"domain_scores_codex":[0.9990494,0.0001733982,0.0001060499,0.0003046508,0.0002564011,0.0001101973],"domain_scores_gemma":[0.9980808,0.0008022286,0.0001415016,0.0004374728,0.0004203287,0.0001175874],"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":[0.0008216647,0.00008945957,0.0008881183,0.001616431,0.0003534569,0.0006076013,0.0002769281,0.002761148,0.0338711,0.006622109,0.7544599,0.1976322],"study_design_scores_gemma":[0.0004650192,0.0002128373,0.005357406,0.0005535627,0.0002879777,0.003027345,0.0001344781,0.03718945,0.08536984,0.06772225,0.7990785,0.0006013994],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002565503,0.001818636,0.6571977,0.0009586351,0.000642429,0.0006298888,0.04766084,0.2735794,0.01494689],"genre_scores_gemma":[0.02051822,0.00160418,0.7550634,0.001965901,0.0005296201,0.003543763,0.07829819,0.1137224,0.02475442],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1300281,"threshold_uncertainty_score":0.4349872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03672271298316098,"score_gpt":0.246164545987777,"score_spread":0.209441833004616,"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."}}