{"id":"W1963512073","doi":"10.1002/cmr.b.20134","title":"Magnetic resonance imaging with composite (dual) gradients","year":2009,"lang":"en","type":"article","venue":"Concepts in Magnetic Resonance Part B","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Institute of Dental and Craniofacial Research; National Institute on Deafness and Other Communication Disorders; National Institute of Biomedical Imaging and Bioengineering; National Eye Institute; National Institutes of Health","keywords":"Magnetic resonance imaging; Dual (grammatical number); Composite number; Nuclear magnetic resonance; Materials science; Functional magnetic resonance imaging; Psychology; Medicine; Physics; Neuroscience; Art; Radiology; Composite material","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001295496,0.0003122865,0.0004169788,0.000128327,0.0001230992,0.00003594174,0.0002340247,0.00005650909,0.0001344418],"category_scores_gemma":[0.00004515595,0.0002814014,0.00006387399,0.000623785,0.0003834173,0.0001254088,0.00005198031,0.0003974733,0.00004054369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009611427,"about_ca_system_score_gemma":0.00005959674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001479237,"about_ca_topic_score_gemma":0.00000620468,"domain_scores_codex":[0.9978197,0.00005055956,0.0004479616,0.0007246273,0.0003582168,0.0005988861],"domain_scores_gemma":[0.9987409,0.00006783174,0.00009664826,0.0008173408,0.0001035753,0.0001737177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003043884,0.0003769908,0.1340532,0.0000257746,0.000001164963,0.0005124857,0.0002745678,0.00001548347,0.004272985,0.004034756,0.006461828,0.8496664],"study_design_scores_gemma":[0.002154607,0.0007982538,0.5090772,0.0005968297,0.00002633586,0.0002695571,0.00003748275,0.00112546,0.001094493,0.002193254,0.4822751,0.0003514345],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7068531,0.2092482,0.002506879,0.0158763,0.0003117837,0.004674765,0.00007082308,0.001311398,0.05914675],"genre_scores_gemma":[0.9586507,0.001556926,0.03097267,0.004090831,0.0001539548,0.0002228745,0.00002355777,0.00005736696,0.0042711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.849315,"threshold_uncertainty_score":0.9999638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02690392624621368,"score_gpt":0.3257950959079198,"score_spread":0.2988911696617061,"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."}}