{"id":"W1600626401","doi":"10.1002/mrm.24724","title":"A new approach to shimming: The dynamically controlled adaptive current network","year":2013,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Homogeneity (statistics); Shim (computing); Computer science; Magnetic field; Offset (computer science); Electronic engineering; Physics; Engineering","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":[0.0002675221,0.0003240229,0.0002104145,0.0004524209,0.0002686242,0.000392591,0.0007869596,0.0003237326,0.001754799],"category_scores_gemma":[0.0006579774,0.00017793,0.0002133942,0.000289159,0.0004942971,0.0009115505,0.0003413731,0.0004269679,0.0003406137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004001649,"about_ca_system_score_gemma":0.000346262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003563793,"about_ca_topic_score_gemma":0.0006116513,"domain_scores_codex":[0.9998528,0.00003245656,0.000005966348,0.00003621557,0.00006123844,0.00001132566],"domain_scores_gemma":[0.9997602,0.00008867103,0.00004535538,0.00003672651,0.00004994881,0.00001907644],"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":[0.0003182131,0.0001628127,0.001032473,0.0003462677,0.00005086918,0.0001515135,0.0002333851,0.03383435,0.6658769,0.0316154,0.002085647,0.264292],"study_design_scores_gemma":[0.0001407235,0.001326048,0.002142764,0.00008258516,0.0001040015,0.001004709,0.00009220696,0.5132013,0.39466,0.01576442,0.07137433,0.0001069272],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04421556,0.001113492,0.9460568,0.0003323227,0.0001512054,0.0000888133,0.00004365126,0.001202078,0.006796135],"genre_scores_gemma":[0.4736601,0.0007804022,0.5209119,0.0002043831,0.0001259049,0.0001438988,0.00006870538,0.0001302222,0.003974466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001754799,"threshold_uncertainty_score":0.005870342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02614986427057498,"score_gpt":0.3119820738399414,"score_spread":0.2858322095693664,"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."}}