{"id":"W1552126856","doi":"10.1007/978-3-540-85988-8_4","title":"A Distributed Spatio-temporal EEG/MEG Inverse Solver","year":2008,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke","keywords":"Computer science; Solver; Inverse; Inverse problem; Electroencephalography; Magnetoencephalography; Algorithm; Artificial intelligence; Mathematics; Neuroscience; Geometry; Programming language; Mathematical analysis","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":[0.0003288227,0.0002717938,0.0002596918,0.0003054764,0.0004444859,0.0001944497,0.001329955,0.00009167004,0.00003691607],"category_scores_gemma":[0.0004195562,0.0002282364,0.00007868316,0.001941543,0.001191654,0.0006430586,0.0005242664,0.0003829798,0.00006905521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001249822,"about_ca_system_score_gemma":0.0002377073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008414119,"about_ca_topic_score_gemma":0.0001192859,"domain_scores_codex":[0.9971587,0.000109898,0.0003356783,0.001021396,0.0006814189,0.0006928875],"domain_scores_gemma":[0.9985944,0.0004643305,0.0001183105,0.0005628054,0.00008181946,0.0001783308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002016212,0.001130975,0.1685766,0.00009957622,0.00001585212,0.002279937,0.01619252,0.3840632,0.1994292,0.0005856111,0.00350341,0.2239214],"study_design_scores_gemma":[0.000616211,0.0002193497,0.01011752,0.00006418498,0.000002578175,0.0003481404,0.000001248229,0.621863,0.3624575,0.002402283,0.001417385,0.000490645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4763324,0.00001503762,0.5220912,0.000568621,0.0007297428,0.0001251783,0.000007989055,0.000103889,0.0000260292],"genre_scores_gemma":[0.9623988,0.000007434891,0.03428824,0.003102763,0.0001731695,0.000006928454,0.000004949952,0.00001268358,0.000005088943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4878029,"threshold_uncertainty_score":0.9307209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02981628282069011,"score_gpt":0.2627396153959847,"score_spread":0.2329233325752946,"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."}}