{"id":"W2049372174","doi":"10.1155/2011/758973","title":"MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG","year":2011,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":125,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Center for Research Resources; National Institute of Neurological Disorders and Stroke; National Institute on Deafness and Other Communication Disorders; University of California, San Francisco; University of Nottingham; European Commission; National Science Foundation; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Computer science; Toolbox; Visualization; Artificial intelligence; MATLAB; Graphical user interface; Pattern recognition (psychology); Electroencephalography; Nutmeg; Machine learning; Human–computer interaction; Neuroscience","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":[0.002391964,0.001431562,0.001183431,0.001858618,0.0003011657,0.001668785,0.001480581,0.0007489988,0.1125128],"category_scores_gemma":[0.009489639,0.0007385736,0.0008883757,0.001233809,0.0004103006,0.001675458,0.001898475,0.001071762,0.01552352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000345863,"about_ca_system_score_gemma":0.0005531652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001245413,"about_ca_topic_score_gemma":0.001887943,"domain_scores_codex":[0.9994718,0.00011619,0.00008160546,0.00009795458,0.0002061975,0.00002618318],"domain_scores_gemma":[0.9975443,0.001395484,0.0001293296,0.000396404,0.0004713267,0.0000630104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001032331,0.0001496097,0.002126379,0.001751565,0.0003710733,0.001219324,0.000887364,0.02331283,0.0467574,0.01576237,0.3535835,0.5530462],"study_design_scores_gemma":[0.0006757192,0.0003069621,0.01538575,0.0004182371,0.0002223976,0.002715695,0.0002255675,0.5268764,0.08763228,0.06364349,0.3015206,0.0003768474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.006901269,0.0001756559,0.8075792,0.0002868986,0.0001804204,0.0002441332,0.01110546,0.1676468,0.005880327],"genre_scores_gemma":[0.06625737,0.0004138777,0.8372606,0.0004298615,0.0001007251,0.001655285,0.01425395,0.06565497,0.01397333],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1125128,"threshold_uncertainty_score":0.376393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09424586174507826,"score_gpt":0.3178700628391371,"score_spread":0.2236242010940588,"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."}}