{"id":"W2548573968","doi":"10.1101/085548","title":"Bridging multiple scales in the human brain using computational modelling","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"European Commission; James S. McDonnell Foundation","keywords":"Overfitting; Computer science; Connectome; Resting state fMRI; Computational model; Human Connectome Project; Population; Neurophysiology; Bridging (networking); Inference; Artificial intelligence; Neuroscience; Functional connectivity; Psychology","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":[0.0006637147,0.0007434864,0.0005588385,0.0007975123,0.0005455384,0.001826925,0.001028388,0.001439074,0.001682724],"category_scores_gemma":[0.003826732,0.0006132185,0.00107762,0.0005882625,0.001530308,0.001973104,0.001590419,0.001041378,0.0002727718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008927334,"about_ca_system_score_gemma":0.0007656242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009073347,"about_ca_topic_score_gemma":0.007039022,"domain_scores_codex":[0.9997243,0.0001511448,0.000009787636,0.00006465171,0.00003465735,0.00001543474],"domain_scores_gemma":[0.9991657,0.000629282,0.00007390994,0.00006569074,0.00002702659,0.00003842238],"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":[0.00001988219,0.00001219153,0.0007042504,0.00004851511,0.00006055767,0.00009642495,0.0001573714,0.9701318,0.001269076,0.02259395,0.0004235205,0.004482445],"study_design_scores_gemma":[0.000004623827,0.000004450509,0.000257395,0.000008147834,0.000005691517,0.00002227291,0.00001705105,0.9663653,0.00009164005,0.03268727,0.0005287027,0.000007379017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1098794,0.001044888,0.8781623,0.002576787,0.00007474104,0.00007470491,0.0003363692,0.000571895,0.007278943],"genre_scores_gemma":[0.8807115,0.001060387,0.1150052,0.0002716023,0.00008987947,0.0002772224,0.0002026296,0.0002494936,0.002131969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009073347,"threshold_uncertainty_score":0.01804107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05979440411445255,"score_gpt":0.2662639720390134,"score_spread":0.2064695679245609,"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."}}