{"id":"W2049198657","doi":"10.1088/0031-9155/57/19/5909","title":"Sparsity regularization in dynamic elastography","year":2012,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Defense Threat Reduction Agency","keywords":"Magnetic resonance elastography; Tikhonov regularization; Regularization (linguistics); Finite element method; Elastography; Inverse problem; Isotropy; Elasticity (physics); Compressibility; Mathematics; Imaging phantom; Regularization perspectives on support vector machines; Applied mathematics; Mathematical optimization; Algorithm; Computer science; Mathematical analysis; Physics; Acoustics; Mechanics; Artificial intelligence; Ultrasound","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":[0.0008408304,0.0003772455,0.0004737527,0.0004018151,0.0002331719,0.0006623375,0.0004432759,0.000997685,0.000645293],"category_scores_gemma":[0.003162028,0.000330885,0.0004985523,0.0004134339,0.0009377992,0.0009117658,0.000850212,0.0007588479,0.0002426017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002948649,"about_ca_system_score_gemma":0.0003121661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001099712,"about_ca_topic_score_gemma":0.0007212675,"domain_scores_codex":[0.9996071,0.0001811947,0.00001638571,0.00004688147,0.0001279518,0.00002038742],"domain_scores_gemma":[0.9988734,0.0008320852,0.00008035745,0.00008343781,0.0001023397,0.00002832976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001279921,0.00005894846,0.001094559,0.000204756,0.00004827637,0.0003057032,0.0001672485,0.7786744,0.04882884,0.07985427,0.001836571,0.0887985],"study_design_scores_gemma":[0.000009229162,0.00002014359,0.0001383587,0.00000711512,0.000003560494,0.00008046599,0.000009570788,0.9781675,0.003353324,0.01697768,0.001225301,0.000007640555],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01011047,0.0002687801,0.9880055,0.0002735363,0.000024029,0.00001371477,0.00002211605,0.00009542472,0.001186465],"genre_scores_gemma":[0.3864267,0.001104672,0.6084832,0.0001794825,0.0001758731,0.0001269688,0.0001604046,0.0001454878,0.003197218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001099712,"threshold_uncertainty_score":0.004446805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08154411640247117,"score_gpt":0.3518552605560445,"score_spread":0.2703111441535733,"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."}}