{"id":"W4310769340","doi":"10.1016/j.ynirp.2022.100150","title":"Dynamic shimming in the cervical spinal cord for multi-echo gradient-echo imaging at 3 T","year":2022,"lang":"en","type":"article","venue":"Neuroimage Reports","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal","funders":"Canadian Institutes of Health Research; Institut de Valorisation des Données; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Image quality; Spinal cord; Homogeneity (statistics); Shim (computing); Magnetic field; Computer science; SIGNAL (programming language); Acoustics; Physics; Nuclear magnetic resonance; Computer vision; Medicine; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003596319,0.0001405547,0.0001952239,0.00009366663,0.0003420023,0.00001675788,0.0001284444,0.00002160873,0.00004337613],"category_scores_gemma":[0.0001004066,0.00011874,0.0001244025,0.0002720758,0.00005408155,0.00005782051,0.0001755975,0.0003771302,0.000001696527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002071751,"about_ca_system_score_gemma":0.00003904598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002556334,"about_ca_topic_score_gemma":0.00001612119,"domain_scores_codex":[0.998526,0.00003653098,0.0003958281,0.0004689111,0.0002701365,0.000302566],"domain_scores_gemma":[0.9990588,0.00005418736,0.0001852457,0.000597577,0.0000406899,0.00006342791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003298789,0.00525176,0.187502,0.0007032874,0.00004336146,0.0455983,0.00208173,0.0009320212,0.3457666,0.001973693,0.01239673,0.3944518],"study_design_scores_gemma":[0.004401961,0.002907238,0.3140084,0.0002117675,0.0003500248,0.06482043,0.002198837,0.1238559,0.006073269,0.006041287,0.4738525,0.001278398],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7028009,0.0007309327,0.2770615,0.009448375,0.0005347152,0.006120104,0.00005453353,0.0006196898,0.002629275],"genre_scores_gemma":[0.9665035,0.00002202633,0.03030383,0.001505236,0.00003588648,0.001017967,0.00007002382,0.00004157561,0.0004999595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4614558,"threshold_uncertainty_score":0.4842076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0480193859456331,"score_gpt":0.3809117532893443,"score_spread":0.3328923673437112,"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."}}