{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004899211,0.0004485528,0.000397667,0.0009536698,0.0001820648,0.0004578319,0.0004871903,0.000587946,0.00312055],"category_scores_gemma":[0.001030598,0.0003210409,0.0002328881,0.0003109732,0.0002913895,0.0007845381,0.0004335418,0.0003723429,0.0007899842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001980741,"about_ca_system_score_gemma":0.0002669498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002793212,"about_ca_topic_score_gemma":0.0005167316,"domain_scores_codex":[0.9998234,0.00002595451,0.00001147239,0.00004164871,0.00007250432,0.00002517385],"domain_scores_gemma":[0.9995154,0.000167767,0.00005880836,0.00008134593,0.0001301777,0.00004637537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003983792,0.00009266495,0.002734702,0.0003685523,0.00004278102,0.0007605613,0.0001375135,0.009161264,0.8741757,0.003605451,0.001053794,0.1074686],"study_design_scores_gemma":[0.00004751479,0.0006728254,0.008048724,0.00007079331,0.00008457109,0.007692509,0.00008633982,0.09241959,0.8766639,0.001897818,0.01222679,0.00008868182],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.17678,0.002281942,0.811952,0.0003356538,0.00005396693,0.0001502368,0.000370064,0.001643037,0.006433213],"genre_scores_gemma":[0.5911527,0.001301463,0.4031631,0.0001578596,0.00004130422,0.00008151233,0.0005141043,0.0001630737,0.003424881],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00312055,"threshold_uncertainty_score":0.01043928,"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."}}