{"id":"W3041331543","doi":"10.1101/2020.07.08.194290","title":"The Cuban Human Brain Mapping Project population based normative EEG, MRI, and Cognition dataset","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"University of Electronic Science and Technology of China; National Natural Science Foundation of China","keywords":"Neuroimaging; Electroencephalography; Cognition; Psychology; Population; Wechsler Adult Intelligence Scale; Normative; Magnetic resonance imaging; Audiology; Medicine; Psychiatry; Radiology","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":[0.001038033,0.001219634,0.001145307,0.002535924,0.0008115085,0.0009805883,0.00194767,0.0007854818,0.01139395],"category_scores_gemma":[0.003351561,0.0003108564,0.0005116994,0.002769649,0.0003038461,0.0003310835,0.001315041,0.0006403557,0.007026383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001543728,"about_ca_system_score_gemma":0.002052787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1174439,"about_ca_topic_score_gemma":0.1178949,"domain_scores_codex":[0.9994686,0.0001265967,0.0000503039,0.0001678568,0.00008963937,0.00009703166],"domain_scores_gemma":[0.9987791,0.0001358094,0.000081748,0.0003131502,0.0005817612,0.0001083707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001250645,0.0002683486,0.02893718,0.0009519883,0.0003774943,0.0008162296,0.0002557084,0.001510395,0.001772105,0.001118209,0.9277979,0.03494385],"study_design_scores_gemma":[0.0007131043,0.0001944692,0.3905621,0.0007159646,0.0003883829,0.002195752,0.0009937142,0.005298065,0.002206239,0.002461332,0.5940724,0.0001984366],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03179318,0.0005512175,0.001320726,0.0002287594,0.00007476355,0.0002892411,0.9627051,0.0004924668,0.002544521],"genre_scores_gemma":[0.02456613,0.0001351762,0.001258086,0.00006859167,0.000022004,0.001171747,0.9707457,0.00009058911,0.001941928],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1174439,"threshold_uncertainty_score":0.2335206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05017872202678059,"score_gpt":0.2698690891732315,"score_spread":0.2196903671464509,"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."}}