{"id":"W3023667707","doi":"10.1007/s11682-020-00272-z","title":"Cross-paradigm connectivity: reliability, stability, and utility","year":2020,"lang":"en","type":"article","venue":"Brain Imaging and Behavior","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Center for Advancing Translational Sciences; National Institute of Mental Health; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Brain and Behavior Research Foundation","keywords":"Generalizability theory; Identifiability; Connectome; Stability (learning theory); Interpretability; Reliability (semiconductor); Trait; Computer science; Neuroimaging; Artificial intelligence; Functional connectivity; Machine learning; Psychology; Pattern recognition (psychology); Mathematics; Neuroscience; Statistics; Power (physics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.000558135,0.0002412448,0.0002947154,0.00004313041,0.0004868401,0.0002448636,0.0001412523,0.00004820097,0.0001051532],"category_scores_gemma":[0.01353782,0.0002335618,0.00006407916,0.000232524,0.001036939,0.0004569089,0.0004462856,0.0002879029,0.0000137771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003268771,"about_ca_system_score_gemma":0.00004194778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000084837,"about_ca_topic_score_gemma":0.00001531402,"domain_scores_codex":[0.9977137,0.0002427171,0.0002478113,0.001165016,0.0002867734,0.0003440141],"domain_scores_gemma":[0.9947897,0.004553371,0.00006889419,0.000307977,0.00005018556,0.0002299255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009449128,0.0002511651,0.8916569,0.0001061847,0.000003722025,0.0000449187,0.0009195873,8.647166e-7,0.08564387,0.0007111345,0.002025219,0.01854191],"study_design_scores_gemma":[0.001224752,0.0001762953,0.9278782,0.00002017419,0.00005665916,0.0001345399,0.0002407232,0.00185552,0.05285535,0.002192912,0.01283834,0.0005265339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9356996,0.0001829652,0.0001639671,0.06241288,0.0001979224,0.0004010648,0.00007779699,0.0002521763,0.0006115764],"genre_scores_gemma":[0.990828,0.00001624842,0.00009447112,0.008813614,0.00009360714,0.00005889293,0.00000154722,0.00002164083,0.00007195185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05512837,"threshold_uncertainty_score":0.9947715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06296262443696149,"score_gpt":0.3097403051656991,"score_spread":0.2467776807287376,"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."}}