{"id":"W2321813163","doi":"10.1109/embc.2014.6943780","title":"A novel approach to quantification of real and artifactual components of current density imaging for phantom and live heart","year":2014,"lang":"en","type":"article","venue":"","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Toronto General Hospital; Toronto Metropolitan University","funders":"Canadian Institutes of Health Research","keywords":"Imaging phantom; Computer science; Reliability (semiconductor); Current (fluid); Range (aeronautics); Artificial intelligence; Artifact (error); Algorithm; Pattern recognition (psychology); Physics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009824801,0.00005916707,0.000130045,0.00005950069,0.00001566945,0.000005879357,0.00002585677,0.00001469456,3.99818e-7],"category_scores_gemma":[0.000010644,0.0000492054,0.00002231649,0.00008006568,0.0000263807,0.00003341444,0.00001174891,0.00002884498,3.319323e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003388676,"about_ca_system_score_gemma":0.000001538024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006264161,"about_ca_topic_score_gemma":0.000005861421,"domain_scores_codex":[0.9996119,0.000005389566,0.0001267839,0.0001041743,0.00005857429,0.00009318181],"domain_scores_gemma":[0.9997904,0.00004735738,0.00001875826,0.00005952254,0.00003846284,0.0000454861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003491829,0.0001303703,0.0158543,0.0002911464,0.00001579443,8.709566e-9,0.000274685,0.00008989609,0.9517116,0.00142551,0.0001438966,0.03002787],"study_design_scores_gemma":[0.0006670832,0.0001199126,0.2796027,0.00004766627,0.00003313361,0.000005315853,0.00006097581,0.5232941,0.1954119,0.000245243,0.0003095428,0.0002023622],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7839499,0.00008342515,0.2156758,0.00001542813,0.00002302732,0.0001396655,0.000005883897,0.00002309898,0.00008372163],"genre_scores_gemma":[0.9916962,0.00002344945,0.008239822,0.000007212614,0.00001340735,0.00000614816,0.000007092015,0.000004730666,0.000001964283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7562997,"threshold_uncertainty_score":0.2006538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03277432445551329,"score_gpt":0.2542960430535217,"score_spread":0.2215217185980084,"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."}}