{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002365017,0.0001440329,0.0001085615,0.0001241134,0.0003985902,0.0001217287,0.0001221109,0.00003381634,0.00005974669],"category_scores_gemma":[0.0004919193,0.0001194227,0.00001233348,0.0004457507,0.0007249776,0.0004702977,0.00005762976,0.0001018831,0.000008079382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001750457,"about_ca_system_score_gemma":0.00005798383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001328455,"about_ca_topic_score_gemma":0.000004390863,"domain_scores_codex":[0.9983615,0.0001320544,0.0002320349,0.0006233713,0.0004576448,0.0001933467],"domain_scores_gemma":[0.9992178,0.0002681822,0.0001097048,0.0001018481,0.0001772226,0.0001252061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003443621,0.0004000839,0.02430613,0.0000764389,0.000007513186,0.00004235145,0.002528208,0.06217367,0.04696243,0.5114047,0.0001487758,0.3516054],"study_design_scores_gemma":[0.0001663819,0.0004820251,0.04559358,0.00002768901,0.00001943529,0.0006845336,0.0001319658,0.8879759,0.01389642,0.05057129,0.0001867662,0.000264005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3989506,0.00002422436,0.5997947,0.00008850211,0.0002568373,0.0002662062,0.000009395299,0.00005929614,0.0005502474],"genre_scores_gemma":[0.997128,0.00007106395,0.001705462,0.0009742599,0.0000181816,0.00001736229,0.000004054373,0.00001121761,0.00007041829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8258022,"threshold_uncertainty_score":0.4869917,"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."}}