{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005936153,0.0005047512,0.0004930198,0.0005365824,0.0001919365,0.001053157,0.0005167999,0.0007660646,0.001021206],"category_scores_gemma":[0.001096594,0.0003478585,0.0002173151,0.0003414233,0.0005931836,0.001430155,0.001051684,0.0006696819,0.0007453298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001717701,"about_ca_system_score_gemma":0.0002170174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009708633,"about_ca_topic_score_gemma":0.0002854602,"domain_scores_codex":[0.9997517,0.00006688825,0.00001224048,0.00007147519,0.00007039728,0.00002715183],"domain_scores_gemma":[0.9995597,0.0001511858,0.00006478628,0.00006362039,0.00008240827,0.00007815105],"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":[0.0006164832,0.00008388581,0.001260054,0.0003823993,0.00005734996,0.0005851135,0.00007735311,0.004303799,0.8379308,0.009818972,0.001608755,0.1432751],"study_design_scores_gemma":[0.0001954273,0.002894784,0.008471808,0.0001716336,0.0002511306,0.01249505,0.00009269371,0.09814541,0.7930277,0.02918437,0.05484527,0.0002248121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1221152,0.00578827,0.8617226,0.0005570973,0.0002088988,0.0001113634,0.00006684822,0.001092103,0.008337514],"genre_scores_gemma":[0.464173,0.002820807,0.5275075,0.000430499,0.0002426585,0.0001251539,0.00007774656,0.0001762624,0.004446322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001053157,"threshold_uncertainty_score":0.0034163,"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."}}