{"id":"W4214623386","doi":"10.1101/2022.02.23.481601","title":"Performance reserves in brain-imaging-based phenotype prediction","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital","funders":"National Institutes of Health; Canada First Research Excellence Fund; European Commission; Deutschen Multiple Sklerose Gesellschaft; Deutsche Forschungsgemeinschaft; Berlin Institute of Health; National Alliance for Research on Schizophrenia and Depression; Canadian Institute for Advanced Research","keywords":"Neuroimaging; Sample size determination; Predictive modelling; Machine learning; Computer science; Artificial intelligence; Modalities; Sample (material); Large sample; Psychology; Neuroscience; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04631919,0.001668254,0.002238117,0.001285894,0.0008831662,0.003039758,0.002336907,0.00272813,0.002749754],"category_scores_gemma":[0.1129448,0.0006464639,0.00127939,0.001288871,0.002350896,0.003640138,0.002939682,0.003631627,0.002180575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083057,"about_ca_system_score_gemma":0.001749322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005986008,"about_ca_topic_score_gemma":0.003424272,"domain_scores_codex":[0.981753,0.01225359,0.000640229,0.003159894,0.001735054,0.0004583328],"domain_scores_gemma":[0.8502851,0.1279274,0.002805178,0.01350587,0.003835001,0.001641422],"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":[0.009885576,0.001093479,0.1697333,0.001176583,0.002771685,0.0005855387,0.0005969228,0.4129791,0.0200404,0.01125087,0.02951709,0.3403695],"study_design_scores_gemma":[0.0001835599,0.0006431981,0.0319283,0.0002138071,0.0002162185,0.0002890734,0.0001892116,0.9215722,0.008739776,0.03268052,0.003242822,0.0001014147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5601224,0.01370756,0.3807786,0.01903105,0.001015961,0.0003427535,0.004871253,0.009034939,0.0110955],"genre_scores_gemma":[0.9380567,0.0007255313,0.0544258,0.001229588,0.0002064804,0.0001846865,0.003484545,0.0004807323,0.00120605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04631919,"threshold_uncertainty_score":0.2449622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02566921154396938,"score_gpt":0.23346866024957,"score_spread":0.2077994487056006,"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."}}