{"id":"W2171345267","doi":"10.1109/tmi.2008.2012161","title":"Multislice Radio-Frequency Current Density Imaging","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multislice; Imaging phantom; Radio frequency; Magnetic resonance imaging; Nuclear magnetic resonance; Noise (video); Physics; Optics; Computer science; Artificial intelligence; Radiology; Medicine; Image (mathematics); Telecommunications","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":[0.0001826666,0.0002460074,0.0002218262,0.0002354494,0.0001724852,0.00004464248,0.0002402854,0.00005795421,0.0001701938],"category_scores_gemma":[0.0000146225,0.0002260572,0.000182851,0.0005827672,0.00009112139,0.0002287175,6.803255e-7,0.0009009383,0.00007903401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009134685,"about_ca_system_score_gemma":0.00003086716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002423023,"about_ca_topic_score_gemma":0.000009082517,"domain_scores_codex":[0.9982917,0.00003817052,0.0003066476,0.0002966849,0.0005411048,0.0005256394],"domain_scores_gemma":[0.9992432,0.00008505853,0.0000224146,0.0002309272,0.00004469498,0.0003736822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006558148,0.0001551816,0.0002839048,0.00002422379,0.00001708212,0.00006637684,0.00007545935,0.0005893104,0.006694818,0.00002854694,0.001104773,0.9909537],"study_design_scores_gemma":[0.002604919,0.0001017385,0.007503353,0.0006441426,0.000196527,0.0004745167,0.00007129475,0.9050969,0.0729759,0.002318873,0.006319751,0.00169204],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02630777,0.002300519,0.9664097,0.001655865,0.001094774,0.0001495525,0.000006544331,0.001064938,0.001010379],"genre_scores_gemma":[0.9974253,0.0007263404,0.0009846202,0.0006201492,0.0001888931,0.00001132875,0.000002239424,0.0000239051,0.00001727297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9892617,"threshold_uncertainty_score":0.9218343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00625409404747915,"score_gpt":0.237407472840655,"score_spread":0.2311533787931759,"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."}}