{"id":"W2042124044","doi":"10.1002/jmri.21171","title":"Measurement of signal‐to‐noise ratios in sum‐of‐squares MR images","year":2007,"lang":"en","type":"letter","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Standard deviation; Signal-to-noise ratio (imaging); Noise (video); Mathematics; SIGNAL (programming language); Region of interest; Statistics; Computer science; Algorithm; Artificial intelligence; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009783678,0.0002698329,0.0008717765,0.0007250095,0.0000244358,0.00001295538,0.0003130271,0.0001486574,0.00008707841],"category_scores_gemma":[0.0002231808,0.0002341305,0.0002430449,0.0004179628,0.0001410844,0.00009984872,0.00005252644,0.001202518,0.000002206945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002239837,"about_ca_system_score_gemma":0.000284822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004130234,"about_ca_topic_score_gemma":0.000004121793,"domain_scores_codex":[0.9966916,0.00005772623,0.001487604,0.0002453162,0.001173976,0.0003438075],"domain_scores_gemma":[0.9972594,0.0001159266,0.001024161,0.0004014111,0.001104895,0.00009418088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002984585,0.0002639079,0.007853438,0.0006981091,0.00001515178,0.001037466,0.000203523,0.0001143494,0.1436505,0.00001683432,0.6204134,0.2254349],"study_design_scores_gemma":[0.001739299,0.0008549122,0.02359851,0.008095831,0.0002247214,0.0004289823,0.0001249256,0.00009797057,0.09452432,0.0005355609,0.8693897,0.0003852931],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.008938098,0.1515891,0.1596851,0.6729628,0.0003286482,0.002972475,0.00008030933,0.00006028981,0.003383217],"genre_scores_gemma":[0.2505729,0.008722683,0.4809652,0.2512807,0.00543625,0.0001861875,0.0000311117,0.0004096837,0.002395367],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.4216821,"threshold_uncertainty_score":0.9547565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02159596252504097,"score_gpt":0.3076331374741292,"score_spread":0.2860371749490882,"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."}}