{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002349523,0.00005206187,0.0001284236,0.0004681993,0.0000195374,0.00002557271,0.0001533244,0.00004536697,0.000001550043],"category_scores_gemma":[0.0003332446,0.00004660262,0.00001267928,0.0001060825,0.00008516007,0.000165793,0.00007468792,0.0002102856,3.271866e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001824506,"about_ca_system_score_gemma":0.00001320542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001388896,"about_ca_topic_score_gemma":1.417492e-7,"domain_scores_codex":[0.9993652,0.00001647053,0.0002373926,0.0001182767,0.000166534,0.00009609576],"domain_scores_gemma":[0.9993986,0.0002134237,0.0001348362,0.000057131,0.0001673446,0.00002860753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004251031,0.00001266924,0.000450604,0.00001875606,0.000009524298,0.000006775774,0.00006639894,0.001680091,0.9820582,0.004611719,0.000002082004,0.01104071],"study_design_scores_gemma":[0.001739634,0.0005202167,0.00121849,0.0003924897,0.000006405568,0.001263858,0.0002948022,0.04953862,0.9372957,0.005360702,0.00223875,0.0001303708],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993431,0.0006718328,0.004164217,0.001311965,0.0003413168,0.00005553537,0.000002810154,0.00001185569,0.000009519961],"genre_scores_gemma":[0.9976956,0.0002935227,0.001929242,0.00002042166,0.00003969392,7.30286e-7,8.018211e-8,0.000005128308,0.00001560904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04785853,"threshold_uncertainty_score":0.19004,"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."}}