{"id":"W4302774145","doi":"10.1016/j.media.2022.102649","title":"Predicting the evolution trajectory of population-driven connectional brain templates using recurrent multigraph neural networks","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research","keywords":"Multigraph; Computer science; Population; Artificial intelligence; Neuroimaging; Normalization (sociology); Graph; Machine learning; Neuroscience; Biology; Theoretical computer science; Medicine","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":[0.001035715,0.0001572254,0.000326993,0.0003796568,0.001119086,0.00002695982,0.000320165,0.00004883418,0.0006362926],"category_scores_gemma":[0.01257255,0.0001264717,0.0003881164,0.002563386,0.0003320979,0.0001765406,0.0002695877,0.0005270987,8.806126e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002184276,"about_ca_system_score_gemma":0.00005550842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001610639,"about_ca_topic_score_gemma":0.0005414782,"domain_scores_codex":[0.9964756,0.0009561453,0.0004659207,0.0004989032,0.00133048,0.0002729895],"domain_scores_gemma":[0.9913575,0.007926349,0.0002860344,0.0002535526,0.00008717702,0.00008937751],"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":[0.0001050834,0.0002719178,0.325235,0.00001706411,0.0005943206,0.00002472807,0.0006304694,0.6621734,0.007786539,0.0002991052,0.00127129,0.001591003],"study_design_scores_gemma":[0.0002387645,0.00006575559,0.0750225,0.000006403392,0.0003223416,0.00002436122,0.0006706961,0.9232874,0.00009190248,0.000102086,0.00005485728,0.00011296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713179,0.0002147325,0.02195166,0.005536146,0.0005884503,0.0002073037,0.00005986786,0.00007723468,0.00004666947],"genre_scores_gemma":[0.9985321,0.000005529105,0.0001160637,0.001031623,0.0002008539,0.00004642327,0.00003369114,0.0000125796,0.00002118512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2611139,"threshold_uncertainty_score":0.9957449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02842572341770853,"score_gpt":0.2876609425353886,"score_spread":0.25923521911768,"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."}}