{"id":"W4256134341","doi":"10.35940/ijeat.f1078.0886s19","title":"Identification and Research of Adhd and Healthy Controls using Fmri","year":2019,"lang":"en","type":"article","venue":"International Journal of Engineering and Advanced Technology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Voxel; Independent component analysis; Functional magnetic resonance imaging; Artificial intelligence; Resting state fMRI; Pattern recognition (psychology); Computer science; Preprocessor; Neuroimaging; Neuroscience; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006472499,0.0005048405,0.0005757841,0.002387736,0.0008056866,0.0013184,0.0003415857,0.0007339442,0.004207489],"category_scores_gemma":[0.002505028,0.0002355357,0.0002965197,0.0005331963,0.0004554982,0.0005086758,0.0005608986,0.0003916854,0.0004661132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003800323,"about_ca_system_score_gemma":0.0002576328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006697278,"about_ca_topic_score_gemma":0.005820812,"domain_scores_codex":[0.9991919,0.00009398182,0.0001000454,0.0003656071,0.0001626832,0.00008566418],"domain_scores_gemma":[0.9994878,0.0001579531,0.0001162537,0.00006024425,0.00008359565,0.00009408023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003565724,0.001202993,0.9011788,0.0002433073,0.0002925748,0.004853943,0.00347597,0.0002069581,0.02556694,0.001003448,0.0009790909,0.05743037],"study_design_scores_gemma":[0.00004611077,0.0003618326,0.9939481,0.0000224031,0.00007809626,0.00178364,0.0009205943,0.0003739269,0.001207961,0.0003883648,0.0008579867,0.00001091059],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962409,0.0004403168,0.000673313,0.00006538068,0.00001862923,0.0001084046,0.0004521619,0.00003541949,0.00196554],"genre_scores_gemma":[0.9968773,0.0001489996,0.000988225,0.00004805605,0.00001747769,0.00009217857,0.0006998429,0.00001416681,0.001113697],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006697278,"threshold_uncertainty_score":0.01407546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03783249730778404,"score_gpt":0.363140235093328,"score_spread":0.325307737785544,"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."}}