{"id":"W2791871352","doi":"10.1002/mrm.27089","title":"Real‐time correction of respiration‐induced distortions in the human spinal cord using a 24‐channel shim array","year":2018,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Shim (computing); Spinal cord; Magnetic resonance imaging; Respiration; Echo time; Computer science; Biomedical engineering; Nuclear magnetic resonance; Medicine; Physics; Anatomy; Radiology; Surgery; Erectile dysfunction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003899955,0.0004386099,0.0003147703,0.0002221949,0.0001420658,0.0002817355,0.000302162,0.0003671643,0.00137929],"category_scores_gemma":[0.001161095,0.0002315519,0.0001498606,0.0001405274,0.0002411372,0.000301765,0.0002581619,0.0002448516,0.0003911533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001003568,"about_ca_system_score_gemma":0.0003006456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003028752,"about_ca_topic_score_gemma":0.0009630595,"domain_scores_codex":[0.9998814,0.00003936573,0.000006153921,0.000033194,0.00002804673,0.00001177678],"domain_scores_gemma":[0.9997701,0.00007568469,0.00004831299,0.00002873908,0.00005483828,0.00002244037],"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":[0.0006976944,0.0000594851,0.0008191282,0.0002296251,0.00004065269,0.00007986965,0.00006786962,0.001989762,0.9342628,0.0001099828,0.0006588405,0.06098434],"study_design_scores_gemma":[0.0002494772,0.003536768,0.04257912,0.0000605792,0.0003944812,0.002484975,0.0001107511,0.04527201,0.8928453,0.0005335076,0.01180188,0.000131055],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6152421,0.002879503,0.376348,0.0004407959,0.0002663081,0.0002333244,0.0003653229,0.002796974,0.00142769],"genre_scores_gemma":[0.7677776,0.001121385,0.2279414,0.0002058246,0.0001696368,0.0002063167,0.0002248396,0.0002437449,0.002109292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00137929,"threshold_uncertainty_score":0.004614174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06951755325609073,"score_gpt":0.3856399233378644,"score_spread":0.3161223700817737,"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."}}