{"id":"W2070004346","doi":"10.1007/s10334-009-0180-4","title":"Utilizing different methods for visualizing susceptibility from a single multi-gradient echo dataset","year":2009,"lang":"en","type":"article","venue":"Magnetic Resonance Materials in Physics Biology and Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Thomas Hospital","funders":"","keywords":"Contrast (vision); Gradient echo; Artificial intelligence; Sensitivity (control systems); Quantitative susceptibility mapping; Computer science; Nuclear magnetic resonance; Pattern recognition (psychology); Visualization; Image processing; Magnetic resonance imaging; Computer vision; Mathematics; Physics; Materials science; Image (mathematics); Radiology; Medicine","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":[0.002478066,0.002044746,0.001132039,0.003493602,0.0007823909,0.002728862,0.001554803,0.002052354,0.002018992],"category_scores_gemma":[0.009517765,0.0006545837,0.001932103,0.002159245,0.0004959092,0.002286642,0.001414076,0.002225027,0.0008398484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000444441,"about_ca_system_score_gemma":0.001468785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006035504,"about_ca_topic_score_gemma":0.01221588,"domain_scores_codex":[0.9995102,0.0001196531,0.00005691159,0.0001478981,0.0001054052,0.00005983723],"domain_scores_gemma":[0.9962465,0.001444048,0.0002427818,0.0008054038,0.0009958508,0.000265476],"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":[0.001515265,0.0004816904,0.005193911,0.001250703,0.001461873,0.0003406588,0.0004990131,0.1238398,0.3681366,0.003777654,0.0115943,0.4819085],"study_design_scores_gemma":[0.0001446329,0.000197275,0.006800707,0.00006380253,0.0004886321,0.0006834794,0.0001804251,0.8460907,0.1291613,0.01009156,0.005832346,0.0002651218],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04329025,0.0003512769,0.9460534,0.0004209931,0.000106526,0.0001366176,0.001841513,0.007226441,0.0005728586],"genre_scores_gemma":[0.1071271,0.0003374181,0.8852708,0.0001164197,0.00008183894,0.000151295,0.003689812,0.002065321,0.001159916],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006035504,"threshold_uncertainty_score":0.01310539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0759341555972293,"score_gpt":0.4502096907313275,"score_spread":0.3742755351340983,"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."}}