{"id":"W2917408107","doi":"10.3389/fncom.2019.00004","title":"Retooling Computational Techniques for EEG-Based Neurocognitive Modeling of Children's Data, Validity and Prospects for Learning and Education","year":2019,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Neurocognitive; Electroencephalography; Computer science; Artificial intelligence; Machine learning; Psychology; Cognition; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002381308,0.001086658,0.0007268292,0.001519167,0.0005194257,0.002718003,0.002411619,0.0008001358,0.005676834],"category_scores_gemma":[0.01410396,0.0007982958,0.001923223,0.000811608,0.001246217,0.002752415,0.002910602,0.00232775,0.001097767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064147,"about_ca_system_score_gemma":0.001543566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007038419,"about_ca_topic_score_gemma":0.006766229,"domain_scores_codex":[0.9993113,0.0002590914,0.00006781024,0.0001422076,0.0001873296,0.00003222924],"domain_scores_gemma":[0.9943737,0.003955249,0.000231486,0.001005976,0.0003416163,0.00009201557],"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":[0.0001295497,0.0001466074,0.004713241,0.000382827,0.0004842664,0.0002947618,0.001471247,0.6082679,0.006583645,0.157364,0.003983212,0.2161787],"study_design_scores_gemma":[0.00001039851,0.00001231311,0.0003382169,0.00003990133,0.00001395974,0.0000387686,0.0000639865,0.9470971,0.00143511,0.04629393,0.004639558,0.00001673043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004927695,0.00006580893,0.990975,0.0001917116,0.00002069739,0.00003607383,0.0001187864,0.002842567,0.0008216135],"genre_scores_gemma":[0.1257852,0.0003034018,0.8711227,0.00009867666,0.0000287919,0.0004331633,0.000345739,0.0007906558,0.001091716],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007038419,"threshold_uncertainty_score":0.01899093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0377693922625317,"score_gpt":0.3113370543928734,"score_spread":0.2735676621303417,"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."}}