{"id":"W3042831418","doi":"10.1016/j.neuroimage.2020.117156","title":"Dynamic Functional Connectivity between order and randomness and its evolution across the human adult lifespan","year":2020,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital","funders":"H2020 European Institute of Innovation and Technology; H2020 Marie Skłodowska-Curie Actions; Berlin Institute of Health; Centre National de la Recherche Scientifique; European Commission; Horizon 2020 Framework Programme; Deutsche Forschungsgemeinschaft; H2020 European Research Council; Agencia Nacional de Investigación e Innovación; James S. McDonnell Foundation","keywords":"Randomness; Control reconfiguration; Merge (version control); Computer science; Statistical physics; Dynamic network analysis; Mathematics; Physics; Statistics","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":[0.0002403861,0.0002170352,0.0002496021,0.00003025557,0.001197309,0.0001261229,0.0001405584,0.00005644724,0.00001600376],"category_scores_gemma":[0.01094651,0.0001715513,0.00004708615,0.0003549331,0.0003657667,0.0003643562,0.0003337344,0.0003712914,0.00002761439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002973179,"about_ca_system_score_gemma":0.00002573681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000210483,"about_ca_topic_score_gemma":0.00005172933,"domain_scores_codex":[0.9980754,0.0003367994,0.0001831961,0.0007658423,0.0003383732,0.0003003992],"domain_scores_gemma":[0.9953383,0.004158905,0.00009312147,0.0001714629,0.0001241662,0.0001139947],"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":[0.0007022033,0.0001398215,0.09609316,0.0002041711,0.00006942046,0.0000560332,0.00231216,0.0001589056,0.8846105,0.01003818,0.003217684,0.002397805],"study_design_scores_gemma":[0.00329873,0.0002693929,0.978595,0.00001245235,0.00004530871,0.0000576266,0.0002186383,0.007016932,0.00732022,0.001074254,0.001747839,0.0003436458],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973568,0.00009205656,0.001029167,0.02416919,0.0002062761,0.0004103089,0.0001175816,0.0001451423,0.0002622895],"genre_scores_gemma":[0.994113,0.00002240101,0.000008432436,0.005482892,0.0001858707,0.00003556807,0.000003297005,0.00002564512,0.0001228362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8825018,"threshold_uncertainty_score":0.9973847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04671509995558275,"score_gpt":0.2886739056097478,"score_spread":0.2419588056541651,"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."}}