{"id":"W2483990523","doi":"10.1002/mrm.26334","title":"Field inhomogeneity correction for gradient echo myelin water fraction imaging","year":2016,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Siemens (Canada); University of Calgary; McGill University Health Centre; McGill University; McGill Genome Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Echo (communications protocol); Nuclear magnetic resonance; Gradient echo; Magnetic resonance imaging; Mathematics; Nuclear medicine; Physics; Computer science; Medicine; 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":[0.0006940891,0.0006683427,0.0003408026,0.0005627893,0.0002212039,0.0004384918,0.0006959895,0.0005587605,0.0009860599],"category_scores_gemma":[0.002171852,0.000292723,0.0003816543,0.0003690144,0.0003153172,0.0004507441,0.0004610568,0.0005836439,0.0004562013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003178153,"about_ca_system_score_gemma":0.0005741278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001636668,"about_ca_topic_score_gemma":0.002537298,"domain_scores_codex":[0.9997784,0.00006356207,0.00001165926,0.00004012287,0.00009232172,0.00001382413],"domain_scores_gemma":[0.9996233,0.0001416487,0.00007467378,0.00004452839,0.00009695242,0.00001875333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00024193,0.00005690059,0.00308698,0.000514756,0.0001583831,0.0004781606,0.0001452867,0.1001695,0.4189129,0.007927584,0.003796546,0.464511],"study_design_scores_gemma":[0.0000293019,0.0001231713,0.003935127,0.0000403613,0.00007940092,0.001187902,0.00002489368,0.7926075,0.1823105,0.004502279,0.01506525,0.00009431684],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01694343,0.0008447586,0.9806918,0.0001322153,0.00004971543,0.0000421822,0.00004976372,0.0007392268,0.0005068194],"genre_scores_gemma":[0.1809566,0.0006690561,0.8161367,0.000142968,0.00004286905,0.0000727993,0.0001480645,0.0004134112,0.001417497],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001636668,"threshold_uncertainty_score":0.003670752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01606950563114381,"score_gpt":0.3181694652811322,"score_spread":0.3020999596499884,"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."}}