{"id":"W4200613736","doi":"10.1038/s41593-021-00962-x","title":"A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence","year":2021,"lang":"en","type":"article","venue":"Nature Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":567,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institutes of Health","keywords":"Cognitive neuroscience; Functional magnetic resonance imaging; Computer science; Computational neuroscience; Artificial intelligence; Systems neuroscience; Cognition; Perception; Neuroscience; Visual perception; Elementary cognitive task; Psychology","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","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003838653,0.0003595206,0.0003039714,0.0002375554,0.0009764058,0.0004647441,0.0007251797,0.0001474056,0.00002415379],"category_scores_gemma":[0.0769859,0.0003513985,0.00006335216,0.002880736,0.001100603,0.000773571,0.001206425,0.001010164,0.00008195254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005059078,"about_ca_system_score_gemma":0.0002497706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009733355,"about_ca_topic_score_gemma":0.00004467477,"domain_scores_codex":[0.9950179,0.0002950632,0.0003282339,0.002478131,0.001125202,0.0007555122],"domain_scores_gemma":[0.9949625,0.003741258,0.0001279678,0.0005591771,0.0002314663,0.0003776244],"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":[0.00009415882,0.0002072869,0.0002920103,0.00001959868,0.000001121495,0.0008271884,0.0002491381,0.0001863747,0.9633656,0.01899489,0.007334758,0.008427931],"study_design_scores_gemma":[0.000180886,0.0005339429,0.03816142,0.0000770474,0.00002497101,0.000832652,0.0002204702,0.003170586,0.9088335,0.006181094,0.04094703,0.0008363807],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8087792,0.0006115027,0.0338453,0.1278491,0.01447478,0.00239133,0.00745249,0.0006783557,0.003918008],"genre_scores_gemma":[0.9016262,0.0000622571,0.0001711661,0.09775119,0.0001475187,0.00004097939,0.0000118863,0.00002235372,0.0001664924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.092847,"threshold_uncertainty_score":0.9998938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07262308887660177,"score_gpt":0.3398607173733919,"score_spread":0.2672376284967901,"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."}}