{"id":"W4322621344","doi":"10.1016/j.brain.2023.100064","title":"Making movies of children's cortical electrical potentials: A practical procedure for dynamic source localization analysis with validating simulation","year":2023,"lang":"en","type":"article","venue":"Brain Multiphysics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's Hospital of Eastern Ontario; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale; Defence Research and Development Canada","keywords":"Computer science; Electroencephalography; Task (project management); Flexibility (engineering); MATLAB; Cognition; Artificial intelligence; Machine learning; Pattern recognition (psychology); Human–computer interaction; Psychology; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.000368921,0.0001941091,0.0003491624,0.0002596859,0.0003881849,0.00005304914,0.0001078039,0.00007721208,0.000003016193],"category_scores_gemma":[0.02770013,0.0001747651,0.000161834,0.002768472,0.000190484,0.0002581202,0.00007569069,0.0001704996,0.000006125099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006111478,"about_ca_system_score_gemma":0.00008093135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005946087,"about_ca_topic_score_gemma":0.00001263112,"domain_scores_codex":[0.9979879,0.0002238001,0.0003269661,0.0005916154,0.0005331932,0.0003364904],"domain_scores_gemma":[0.985225,0.01404207,0.0002797994,0.0002061072,0.0002055787,0.00004149911],"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":[0.00027271,0.0002020864,0.01239195,0.00006081668,0.0002627443,0.000002636255,0.0006597699,0.9539029,0.02736138,0.002495706,0.0001612093,0.002226106],"study_design_scores_gemma":[0.0005133346,0.0001671868,0.01631539,0.00002571757,0.0003067569,0.000003795571,0.0002194602,0.9753837,0.006289805,0.0005311784,0.00004386598,0.0001998356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3645285,0.000003172185,0.6337863,0.0009464742,0.00002491737,0.0005113431,0.00002432132,0.0001638101,0.00001115185],"genre_scores_gemma":[0.9965358,0.000001999926,0.002586563,0.0005802414,0.00007215361,0.00007381283,0.00005645742,0.00003916711,0.00005378174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6320074,"threshold_uncertainty_score":0.98049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0527910328552855,"score_gpt":0.3554375482431999,"score_spread":0.3026465153879144,"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."}}