{"id":"W2921134818","doi":"10.3389/fninf.2019.00014","title":"Visbrain: A Multi-Purpose GPU-Accelerated Open-Source Suite for Multimodal Brain Data Visualization","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Sleep & Circadian Network; Hôpital du Sacré-Cœur de Montréal; Université de Montréal","funders":"","keywords":"Computer science; Visualization; Python (programming language); Suite; Graphical user interface; Modular design; Human–computer interaction; Artificial intelligence; 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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007733224,0.0003450927,0.0005044572,0.0003551188,0.0002810767,0.0004090601,0.001774344,0.0001198997,0.00001974388],"category_scores_gemma":[0.01474621,0.0003543528,0.00006548233,0.0008407997,0.000129568,0.002740147,0.001434203,0.0002800918,0.00007738789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001231215,"about_ca_system_score_gemma":0.0001337665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002065444,"about_ca_topic_score_gemma":0.00002749495,"domain_scores_codex":[0.9973336,0.0001901064,0.0007255469,0.0007426106,0.0004275714,0.0005805483],"domain_scores_gemma":[0.9955016,0.002796719,0.0003410087,0.001154422,0.00009985331,0.0001063471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001852251,0.001545552,0.04407848,0.001208756,0.0001029877,0.00003314615,0.008823982,0.0313369,0.03307449,0.003642923,0.8413054,0.03299509],"study_design_scores_gemma":[0.003538726,0.000213176,0.00123264,0.00004604122,0.0000100298,0.00001106062,0.0005458471,0.85142,0.002866487,0.0001386366,0.139586,0.0003914016],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2562715,0.00009508424,0.7128679,0.006308655,0.00790929,0.01190383,0.001203086,0.0006587544,0.002781878],"genre_scores_gemma":[0.7327741,0.0002189838,0.1749354,0.0755963,0.0004016013,0.0006677779,0.001464823,0.0004943138,0.01344669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8200831,"threshold_uncertainty_score":0.9998909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09347454226708482,"score_gpt":0.3347221976409382,"score_spread":0.2412476553738534,"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."}}