{"id":"W4310113667","doi":"10.7554/elife.77599","title":"Improving the accuracy of single-trial fMRI response estimates using GLMsingle","year":2022,"lang":"en","type":"article","venue":"eLife","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":171,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Science Foundation","keywords":"Functional magnetic resonance imaging; Computer science; Voxel; Artificial intelligence; Replicate; Python (programming language); Machine learning; Generalizability theory; Putamen; Pattern recognition (psychology); Neuroscience; Statistics; Psychology; Mathematics","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":[0.0006379355,0.00009062882,0.0001083074,0.00006601297,0.0006004677,0.00005916515,0.0002203682,0.00001843004,0.00006553622],"category_scores_gemma":[0.004581131,0.00006944887,0.00006853719,0.0003329875,0.00008029999,0.0001294098,0.0002896751,0.0001704766,0.000003714885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007497836,"about_ca_system_score_gemma":0.0000832963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005831751,"about_ca_topic_score_gemma":0.000001612763,"domain_scores_codex":[0.9986199,0.0003126572,0.0002253669,0.0002401268,0.000413276,0.0001887209],"domain_scores_gemma":[0.9978746,0.001578552,0.0002261985,0.0002635952,0.00002694321,0.00003008956],"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":[0.005293222,0.00007801972,0.00002973388,0.000004230403,0.000001469023,0.00000965299,0.0001058204,0.002190859,0.9907222,0.0001677466,0.0001100269,0.001287057],"study_design_scores_gemma":[0.004207943,0.00102824,0.0002572893,0.00001122786,0.0000234592,0.00009987885,0.0001884505,0.1715265,0.818098,0.0002433226,0.004105986,0.0002096513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974992,0.00002258082,0.0003979054,0.0005094184,0.001153217,0.0002771734,0.00001725462,0.00004817952,0.00007513047],"genre_scores_gemma":[0.9987861,0.000001136257,0.000235106,0.000733637,0.0001109464,0.00001255364,9.842601e-7,0.00001648412,0.000103068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1726241,"threshold_uncertainty_score":0.5484373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06634597806164595,"score_gpt":0.2930887198812631,"score_spread":0.2267427418196171,"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."}}