{"id":"W2914176589","doi":"10.1111/jon.12603","title":"Variability of Resting‐State Functional MRI Graph Theory Metrics across 3T Platforms","year":2019,"lang":"en","type":"article","venue":"Journal of Neuroimaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Mental Health","keywords":"Intraclass correlation; Medicine; Clustering coefficient; Resting state fMRI; Connectome; Graph theory; Nuclear medicine; Functional connectivity; Artificial intelligence; Computer science; Cluster analysis; Neuroscience; Psychology; Mathematics; Radiology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003103861,0.0001900776,0.0004025533,0.0003447457,0.0002002224,0.0000598277,0.0003399435,0.00003470871,0.00005187928],"category_scores_gemma":[0.02609323,0.0001535636,0.0002594228,0.001007467,0.0002444612,0.0007833038,0.0002187578,0.0006315504,0.00001764631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007836332,"about_ca_system_score_gemma":0.0001128635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003777941,"about_ca_topic_score_gemma":7.235192e-7,"domain_scores_codex":[0.9974305,0.0002400706,0.0007024469,0.0003792764,0.0009031948,0.00034448],"domain_scores_gemma":[0.9831243,0.01521777,0.0008461621,0.0003142488,0.0004060394,0.00009146854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001680079,0.0005617953,0.4151207,0.0002418635,0.0001229019,0.0002020167,0.001343591,0.01669043,0.5440487,0.01253435,0.00253979,0.004913769],"study_design_scores_gemma":[0.003577204,0.001141783,0.6079382,0.000218532,0.00009194254,0.002156561,0.0006704652,0.003697329,0.09129293,0.2837974,0.004721255,0.0006963834],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861739,0.00005983885,0.008859138,0.001155833,0.00216355,0.0001403447,0.00001940351,0.00002999902,0.001398039],"genre_scores_gemma":[0.9978151,0.00003684811,0.0004352058,0.001317969,0.0001343372,0.000001257233,2.343128e-7,0.00002380968,0.0002351771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4527558,"threshold_uncertainty_score":0.9821104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04514041048609786,"score_gpt":0.2892725326505827,"score_spread":0.2441321221644849,"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."}}