{"id":"W2127628656","doi":"","title":"Volumetric imaging of cardiac current sources using Lp-norm regularization","year":2013,"lang":"en","type":"article","venue":"Computing in Cardiology Conference","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Regularization (linguistics); Current (fluid); Norm (philosophy); Computer science; Current source; Iterative reconstruction; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001841206,0.0001081615,0.0004049359,0.0002048911,0.00005138139,0.000009129639,0.0001028102,0.0000618356,0.000005899465],"category_scores_gemma":[0.00009180524,0.000106012,0.00008054538,0.0004013751,0.0001394655,0.00005204199,0.0001013306,0.0001959369,0.00000363895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006327202,"about_ca_system_score_gemma":0.0000798472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004093062,"about_ca_topic_score_gemma":1.832584e-7,"domain_scores_codex":[0.9990855,0.00005674038,0.0003105031,0.0002510137,0.00009016141,0.0002061128],"domain_scores_gemma":[0.9991782,0.00008667705,0.000148136,0.0002728096,0.0002671914,0.00004695878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005173895,0.00003339609,0.6488066,0.00007145758,0.00001588817,0.000001924357,0.0001683109,0.003345875,0.02004511,0.001974541,0.0001464452,0.3253852],"study_design_scores_gemma":[0.0004968011,0.00006773234,0.5670401,0.0004803049,0.00009996233,0.00005816829,0.0002340341,0.4179332,0.004579719,0.0067174,0.00196808,0.0003244594],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4368688,0.001311899,0.5602904,0.00006808866,0.0001093969,0.0003481935,0.000001928531,0.00005958449,0.0009417403],"genre_scores_gemma":[0.9719952,0.0001169486,0.02768994,0.00003107573,0.0001093035,0.00001503061,0.00001742177,0.00001079012,0.00001424492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5351265,"threshold_uncertainty_score":0.4323045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03527941383761679,"score_gpt":0.3255460316930774,"score_spread":0.2902666178554607,"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."}}