{"id":"W6931086268","doi":"10.5281/zenodo.3241716","title":"fMRIPrep: a robust preprocessing pipeline for functional MRI","year":2019,"lang":"en","type":"other","venue":"Figshare","topic":"Cellular transport and secretion","field":"Biochemistry, Genetics and Molecular Biology","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":[0.002410131,0.002983823,0.00131636,0.002637173,0.001423852,0.003339675,0.00359473,0.002415271,0.1227725],"category_scores_gemma":[0.007457145,0.002140479,0.001844886,0.001638271,0.0006963179,0.001620861,0.003025995,0.00315855,0.08642707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006575145,"about_ca_system_score_gemma":0.001979446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002087541,"about_ca_topic_score_gemma":0.005450215,"domain_scores_codex":[0.9992974,0.0001277745,0.00008356338,0.0002232422,0.0001879296,0.0000801505],"domain_scores_gemma":[0.9983553,0.0007157525,0.000112214,0.0003630569,0.0003459295,0.0001076014],"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":[0.0009101907,0.00008633862,0.000845152,0.001773568,0.0003741861,0.0006916107,0.0002940854,0.003415005,0.04495215,0.00733042,0.7346328,0.2046944],"study_design_scores_gemma":[0.0006086342,0.0002499488,0.00678918,0.0005625415,0.0003410874,0.003960846,0.0001709104,0.04925187,0.102998,0.08194645,0.7523967,0.0007237084],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002612334,0.001431515,0.7155604,0.0008798449,0.0005327849,0.0006391672,0.04563143,0.2214712,0.01124135],"genre_scores_gemma":[0.01996827,0.001433327,0.7800883,0.001686356,0.0004569234,0.003928301,0.06755933,0.1052175,0.0196616],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1227725,"threshold_uncertainty_score":0.410715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02921398821776166,"score_gpt":0.2354927567731145,"score_spread":0.2062787685553528,"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."}}