{"id":"W2758210255","doi":"10.1111/biom.12782","title":"Fully Bayesian Spectral Methods for Imaging Data","year":2017,"lang":"en","type":"article","venue":"Biometrics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Northern California Institute for Research and Education; National Institute for Health and Care Research; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute on Aging; National Science Foundation; Canadian Institutes of Health Research; University of Southern California; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; National Institutes of Health","keywords":"Markov chain Monte Carlo; Computer science; Bayesian probability; Bayesian inference; Inference; Markov chain; Sampling (signal processing); Pattern recognition (psychology); Spatial analysis; Artificial intelligence; Data mining; Algorithm; Statistics; Machine learning; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001139625,0.0001246741,0.0001632336,0.0005818523,0.0009781009,0.000333563,0.001373298,0.00003172112,0.00001657222],"category_scores_gemma":[0.1258494,0.0001175819,0.00005794038,0.0008251893,0.0001960372,0.0005502836,0.0008264997,0.00008342515,0.00002271458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005524421,"about_ca_system_score_gemma":0.00004546713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002498085,"about_ca_topic_score_gemma":0.000005109342,"domain_scores_codex":[0.9985908,0.00007045241,0.0001417035,0.0006721502,0.0002105287,0.000314325],"domain_scores_gemma":[0.9910793,0.007138764,0.0001548832,0.001496703,0.00006121929,0.00006916758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006422832,0.0001530245,0.009154699,0.00006005237,0.0000291264,0.00001980097,0.00005578899,0.000001566913,0.2195524,0.01036361,0.07090271,0.689643],"study_design_scores_gemma":[0.0008825156,0.0001255489,0.03203208,0.00001109607,0.00004350316,0.00003556985,0.00003941579,0.02732566,0.1174072,0.01000563,0.8116205,0.000471209],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002633379,0.000497977,0.959417,0.0216284,0.004105382,0.0005406046,0.0004230361,0.0001845752,0.01056963],"genre_scores_gemma":[0.6412857,0.00008839678,0.3527296,0.003073062,0.0009692791,0.00003983381,0.00001819231,0.00005551962,0.001740398],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7407178,"threshold_uncertainty_score":0.881514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2428644116204526,"score_gpt":0.4521559866076065,"score_spread":0.209291574987154,"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."}}