{"id":"W3009157573","doi":"10.1101/2020.02.28.970673","title":"Moving Beyond the Mean: Subgroups and dimensions of brain activity and cognitive performance across domains","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"Brain and Behavior Research Foundation","keywords":"Human Connectome Project; Bootstrapping (finance); Cluster analysis; Psychology; Cognition; Similarity (geometry); Neuroimaging; Hierarchical clustering; Effects of sleep deprivation on cognitive performance; Cluster (spacecraft); Cognitive psychology; Brain activity and meditation; Correlation; Functional connectivity; Artificial intelligence; Computer science; Mathematics; Neuroscience; Econometrics","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":[0.002002869,0.0002812288,0.0004946905,0.001417459,0.0003226127,0.001145771,0.0003419313,0.0004686996,0.001702838],"category_scores_gemma":[0.007441096,0.0001314509,0.0003307636,0.0009585803,0.0008577569,0.0009849886,0.0009099863,0.0005284902,0.0002254467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001794725,"about_ca_system_score_gemma":0.0001459203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001065281,"about_ca_topic_score_gemma":0.001061475,"domain_scores_codex":[0.999248,0.0002527681,0.00005276061,0.00024204,0.0001373657,0.00006708837],"domain_scores_gemma":[0.9957682,0.00233256,0.0006494353,0.0007293539,0.0002515978,0.0002687825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002025731,0.0002359994,0.8593841,0.0001854111,0.001408215,0.0002569459,0.006524946,0.003617605,0.05856138,0.003107544,0.001896256,0.06279583],"study_design_scores_gemma":[0.000009092711,0.0001301224,0.9874797,0.00001347833,0.00004690239,0.0001620862,0.0009711995,0.004058578,0.001443412,0.005331128,0.0003319118,0.00002237572],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965061,0.00009083453,0.002546506,0.00006956852,0.000004677596,0.000007404636,0.000180938,0.00002858091,0.0005653952],"genre_scores_gemma":[0.9993101,0.00001027903,0.0004782815,0.000007519345,0.000003704388,0.000006332636,0.0001137354,0.0000103617,0.00005957889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002002869,"threshold_uncertainty_score":0.01059228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.024294743723202,"score_gpt":0.2457858576833941,"score_spread":0.2214911139601921,"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."}}