{"id":"W2947403850","doi":"","title":"Using EEG Features and Machine Learning to Predict Gifted Children.","year":2019,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Neuroscience, Education and Cognitive Function","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec; Université du Québec à Montréal","funders":"","keywords":"Electroencephalography; Computer science; Machine learning; Artificial intelligence; Psychology; 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":[0.0008073386,0.0005750497,0.0002253948,0.001235252,0.0001156412,0.0007563388,0.0002220824,0.000514698,0.001341623],"category_scores_gemma":[0.005819794,0.0001241985,0.0003356148,0.0005847137,0.0002055064,0.0005362971,0.0004602311,0.000655701,0.0004380445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002074504,"about_ca_system_score_gemma":0.0002100362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002700856,"about_ca_topic_score_gemma":0.002635222,"domain_scores_codex":[0.9998097,0.0000655336,0.00001880321,0.00004154476,0.00004373746,0.00002064916],"domain_scores_gemma":[0.9987926,0.000657263,0.0002458078,0.0000799643,0.0001303444,0.00009401525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001166875,0.0002903052,0.5149563,0.0002247088,0.0004886463,0.001220689,0.0002495029,0.01617745,0.02152881,0.001363502,0.005768647,0.4365646],"study_design_scores_gemma":[0.00009906064,0.0005085568,0.7934359,0.0002383077,0.000207694,0.003594594,0.0005404307,0.1680348,0.01842973,0.01034131,0.004480715,0.00008890474],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.948609,0.0027132,0.04047481,0.001034595,0.0001417316,0.00004325472,0.002717328,0.0006761423,0.003589941],"genre_scores_gemma":[0.9819466,0.0006164954,0.01556966,0.0000450543,0.00003782684,0.0000233806,0.0009008652,0.00002930638,0.0008307486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002700856,"threshold_uncertainty_score":0.005370259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02060669137780435,"score_gpt":0.2709431240237133,"score_spread":0.2503364326459089,"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."}}