{"id":"W2767618708","doi":"10.1371/journal.pone.0198583","title":"An efficient algorithm for estimating brain covariance networks","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; IXICO; H. Lundbeck A/S; Servier; National Institutes of Health; Australian e-Health Research Centre; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Australian Government; F. Hoffmann-La Roche; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Queensland University of Technology; National Institute on Aging; Commonwealth Scientific and Industrial Research Organisation; Foundation for the National Institutes of Health","keywords":"Covariance; Pairwise comparison; Estimator; Algorithm; Sensitivity (control systems); Contrast (vision); Computer science; Consistency (knowledge bases); Range (aeronautics); Sample size determination; Mathematics; Estimation of covariance matrices; Statistics; Artificial intelligence; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002860782,0.001562532,0.001263209,0.001884937,0.0008632183,0.001535762,0.001665715,0.001651499,0.006620177],"category_scores_gemma":[0.01502652,0.000890654,0.001267774,0.001796899,0.0009652651,0.001898425,0.002527876,0.002380439,0.002989901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001043083,"about_ca_system_score_gemma":0.002772721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005360735,"about_ca_topic_score_gemma":0.00855283,"domain_scores_codex":[0.9985077,0.0005622921,0.0001082058,0.0003626652,0.0003646399,0.00009459937],"domain_scores_gemma":[0.9965231,0.002257594,0.0002950368,0.0003068948,0.0005269123,0.00009050905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00018891,0.0000827177,0.00208433,0.0002486504,0.000216806,0.000183892,0.0002295515,0.3066178,0.006323379,0.04594985,0.01317955,0.6246946],"study_design_scores_gemma":[0.0000513254,0.00003940779,0.0005819139,0.00003293219,0.00002248283,0.0001573315,0.00003421494,0.9353886,0.001636805,0.05595058,0.006076258,0.00002814696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007228567,0.0000472306,0.9983437,0.0000670485,0.00001446542,0.00004401583,0.00006848629,0.0004725917,0.0002195986],"genre_scores_gemma":[0.02211889,0.00009620461,0.9753455,0.00009298595,0.00004349037,0.0004773003,0.0005127068,0.0002508275,0.001062021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006620177,"threshold_uncertainty_score":0.02214664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07573008513063086,"score_gpt":0.2843491776983974,"score_spread":0.2086190925677666,"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."}}