{"id":"W4401927128","doi":"10.1002/mrm.30257","title":"On the impact of B0 shimming algorithms on single‐voxel MR spectroscopy","year":2024,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal","funders":"Institut TransMedTech; Natural Sciences and Engineering Research Council of Canada; Polytechnique Montréal","keywords":"Shim (computing); Algorithm; Voxel; Mathematics; Computer science; Full width at half maximum; In vivo magnetic resonance spectroscopy; Nuclear magnetic resonance; Nuclear medicine; Physics; Magnetic resonance imaging; Artificial intelligence; Medicine; Optics; Radiology","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":[0.004037684,0.0006882662,0.0003720869,0.000478671,0.0003442696,0.0005322767,0.000354061,0.0006355383,0.0009346358],"category_scores_gemma":[0.0189705,0.0002290937,0.0002786057,0.0003465926,0.0004484002,0.0006187155,0.000455632,0.000279929,0.0003212411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000185447,"about_ca_system_score_gemma":0.0002654826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007843706,"about_ca_topic_score_gemma":0.001236579,"domain_scores_codex":[0.9985916,0.0007545347,0.0001276583,0.0001964901,0.0002894639,0.00004028702],"domain_scores_gemma":[0.9902788,0.007239963,0.0008276174,0.0005770127,0.0009312495,0.0001453064],"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":[0.01321779,0.0007095534,0.05645482,0.001381919,0.001125431,0.0004684359,0.0008448867,0.03067439,0.4993954,0.0006370953,0.0007596796,0.3943306],"study_design_scores_gemma":[0.0007524543,0.01604942,0.3524102,0.0002848172,0.001585381,0.002822488,0.0003567413,0.1175707,0.501937,0.001893031,0.004114788,0.0002229545],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9444218,0.001595291,0.05225386,0.0001592131,0.00005000192,0.0001056196,0.00008624591,0.0003198031,0.001008022],"genre_scores_gemma":[0.9227694,0.0005659692,0.07570621,0.0001053501,0.00004355064,0.00006667154,0.0001422188,0.0002227182,0.0003778689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004037684,"threshold_uncertainty_score":0.02135354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03078590629464532,"score_gpt":0.36909030463602,"score_spread":0.3383043983413747,"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."}}