{"id":"W4288045497","doi":"10.1016/j.neuroimage.2022.119521","title":"A reusable benchmark of brain-age prediction from M/EEG resting-state signals","year":2022,"lang":"en","type":"article","venue":"NeuroImage","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"InteraXon (Canada)","funders":"Agence Nationale de la Recherche","keywords":"Electroencephalography; Benchmark (surveying); Computer science; Artificial intelligence; Machine learning; Magnetoencephalography; Population; Random forest; Set (abstract data type); Deep learning; Brain activity and meditation; Psychology; Medicine; Neuroscience; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.0003180071,0.0001754112,0.0002357201,0.0001290952,0.0002934932,0.00008007366,0.0006411537,0.00002600699,0.0006692176],"category_scores_gemma":[0.0007319481,0.0001813826,0.00009439381,0.0004359001,0.0001252077,0.0002356774,0.0005368487,0.0004053086,0.00002558024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004202277,"about_ca_system_score_gemma":0.00006316676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001297515,"about_ca_topic_score_gemma":0.000005071668,"domain_scores_codex":[0.9975302,0.0005108635,0.0004122266,0.0006657773,0.0005522246,0.0003287437],"domain_scores_gemma":[0.9980319,0.00111472,0.0002290275,0.000513249,0.00002999999,0.00008109544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006937875,0.000119264,0.0006409356,0.00001510465,0.000003984318,0.0002526916,0.0007894171,0.002269317,0.9759836,0.0000430456,0.01895478,0.0008584516],"study_design_scores_gemma":[0.0009827942,0.001152783,0.01592171,0.00004930807,0.00001947965,0.00008261758,0.00008990042,0.0142404,0.9097413,0.003960646,0.05340661,0.0003524024],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882116,0.00003609037,0.0003255488,0.0008728702,0.0007458211,0.0002636879,0.0005596273,0.0001830521,0.008801742],"genre_scores_gemma":[0.9938764,0.000006111076,0.000354481,0.002339365,0.00007163045,0.00002650338,0.00001775547,0.00003155598,0.003276207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06624227,"threshold_uncertainty_score":0.7396568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03373366838768526,"score_gpt":0.2677588123636365,"score_spread":0.2340251439759513,"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."}}