{"id":"W2916353457","doi":"10.21105/joss.01257","title":"AtlasReader: A Python package to generate coordinate tables, region labels, and informative figures from statistical MRI images","year":2019,"lang":"en","type":"article","venue":"The Journal of Open Source Software","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; Université de Montréal; McGill University; International Laboratory for Brain, Music and Sound Research; Queen's University","funders":"National Institute of Mental Health; Engineering and Physical Sciences Research Council; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Python (programming language); Computer science; Artificial intelligence; R package; Computer graphics (images); Pattern recognition (psychology); Natural language processing; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006463129,0.000231021,0.0005830305,0.0000576878,0.0001688578,0.0002643761,0.0006523399,0.00007224615,0.0004235471],"category_scores_gemma":[0.001109483,0.0001341927,0.00005147378,0.0001891352,0.0001245263,0.0003898446,0.0004653792,0.0004172693,0.0001078059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004505149,"about_ca_system_score_gemma":0.00005447551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002916659,"about_ca_topic_score_gemma":0.000007925719,"domain_scores_codex":[0.9982556,0.0002814599,0.0005856162,0.0001589946,0.0004033455,0.0003150206],"domain_scores_gemma":[0.9959396,0.002846836,0.0004184875,0.0002836926,0.0002418022,0.0002696324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.003771977,0.0005772734,0.008545508,0.0005373702,0.001043478,0.0003870828,0.02787895,0.000532873,0.0009770084,0.009619334,0.7704378,0.1756913],"study_design_scores_gemma":[0.01315566,0.006409395,0.03716739,0.003306629,0.001744543,0.001931137,0.02008051,0.005274996,0.01002079,0.7351221,0.1627289,0.00305797],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2166269,0.000417215,0.7790876,0.00215753,0.0001548861,0.0006663513,0.0004762867,0.00004098933,0.0003722018],"genre_scores_gemma":[0.3556659,0.0005553822,0.6301617,0.003187678,0.0005273142,0.00002379259,0.00006907493,0.0001778256,0.009631351],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7255027,"threshold_uncertainty_score":0.5472219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03129510177915976,"score_gpt":0.30699227697035,"score_spread":0.2756971751911902,"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."}}