{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001470012,0.0001566799,0.0001282882,0.0001972271,0.0002850041,0.0001501956,0.000141547,0.00004623835,0.0001906651],"category_scores_gemma":[0.0007052806,0.0001439501,0.00003250086,0.0005800506,0.00006819223,0.0002482987,0.00009558516,0.000273722,0.0002132184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002889357,"about_ca_system_score_gemma":0.00004787156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002501383,"about_ca_topic_score_gemma":0.000008077975,"domain_scores_codex":[0.998574,0.0001514927,0.0001081241,0.0005803728,0.0003001125,0.0002858643],"domain_scores_gemma":[0.999428,0.0001116641,0.00007534298,0.0001812225,0.00003805853,0.0001657025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008752088,0.00007363343,0.08571148,0.000009646582,0.000002644565,0.000003782804,0.0008120867,0.0003901525,0.9089054,0.0004081962,0.0009745875,0.002620797],"study_design_scores_gemma":[0.0009977103,0.0004794851,0.7511084,0.0000808312,0.00002520825,0.0002762932,0.0004592914,0.005153203,0.2218994,0.00007427997,0.01891208,0.0005337397],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943633,0.00004042069,0.0002724885,0.0009480364,0.0008761708,0.0003944807,0.00001082913,0.0001236439,0.00297063],"genre_scores_gemma":[0.9840476,0.00001997573,0.0001195671,0.002483387,0.00007806592,0.00000715301,0.000003135033,0.00002172553,0.01321941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6870061,"threshold_uncertainty_score":0.5870114,"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."}}