{"id":"W2010892173","doi":"10.1016/j.neuroimage.2010.12.086","title":"Data-driven optimization and evaluation of 2D EPI and 3D PRESTO for BOLD fMRI at 7 Tesla: I. Focal coverage","year":2011,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Baycrest Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Vanderbilt University","keywords":"Reproducibility; Single shot; Data acquisition; Computer science; Functional magnetic resonance imaging; Physics; Image resolution; Artificial intelligence; Pattern recognition (psychology); Nuclear magnetic resonance; Computer vision; Nuclear medicine; Mathematics; Medicine; Optics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001687821,0.00007602703,0.000132023,0.00003355708,0.0000535197,0.000004923211,0.0000569607,0.00004077733,0.00004255978],"category_scores_gemma":[0.0001151276,0.00007052534,0.00001391872,0.00005038637,0.00006771796,0.0001394828,0.0001080649,0.00004986385,6.370581e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001557056,"about_ca_system_score_gemma":0.00002167586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007107484,"about_ca_topic_score_gemma":0.000003813268,"domain_scores_codex":[0.9993449,0.00001990699,0.0001533564,0.0002628297,0.0001322218,0.00008675094],"domain_scores_gemma":[0.9993178,0.00005099382,0.00008638047,0.0003768858,0.0001172481,0.00005074295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002628087,0.002911916,0.02981852,0.001689757,0.0001644391,0.00003933535,0.002318403,0.0216386,0.4093575,0.01254062,0.06284226,0.4540506],"study_design_scores_gemma":[0.003116167,0.0008233492,0.03536939,0.00006549638,0.0005929628,0.00006314937,0.00002179118,0.9317405,0.0155308,0.00143498,0.01103737,0.0002040806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1537696,0.0003157354,0.8375869,0.0004016288,0.00003307431,0.002561829,0.0003887664,0.00009944857,0.004842998],"genre_scores_gemma":[0.6994964,0.0004105403,0.2988932,0.0002457878,0.00005019308,0.0001690487,0.000388551,0.00003299872,0.0003132318],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9101019,"threshold_uncertainty_score":0.287594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1411576005458069,"score_gpt":0.3705093682375305,"score_spread":0.2293517676917236,"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."}}