{"id":"W2406997757","doi":"10.1088/0967-3334/37/6/785","title":"3D EIT image reconstruction with GREIT","year":2016,"lang":"ar","type":"article","venue":"Physiological Measurement","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Electrical impedance tomography; Sensitivity (control systems); Iterative reconstruction; Computer science; Transverse plane; Planar; Computer vision; Plane (geometry); Electrode; Artificial intelligence; Algorithm; Tomography; Physics; Optics; Mathematics; Electronic engineering; Engineering; Computer graphics (images); Medicine; Radiology; Geometry","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":[0.0003163804,0.0004220778,0.0004412557,0.00008323693,0.0001275453,0.00003756185,0.0002318658,0.0001773634,0.000662617],"category_scores_gemma":[0.00007081623,0.0002050288,0.0001873248,0.0004249832,0.0002456785,0.0001793414,0.00004330774,0.0002765629,0.0007721527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002510691,"about_ca_system_score_gemma":0.00003051841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001258098,"about_ca_topic_score_gemma":0.000006166855,"domain_scores_codex":[0.9975321,0.000123304,0.0003788844,0.0005809314,0.0006551699,0.0007296049],"domain_scores_gemma":[0.9990281,0.00005147048,0.00009953464,0.0003383721,0.0002531222,0.0002294162],"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":[0.0001642478,0.0001658717,0.0005254678,0.00008437548,0.000197961,0.00001104508,0.0000119539,0.00002652509,0.7760245,0.0001831962,0.0008303813,0.2217745],"study_design_scores_gemma":[0.01188868,0.01519483,0.3095335,0.006084707,0.001082877,0.0002966913,0.0001153528,0.003677435,0.6083493,0.01971579,0.01727563,0.006785231],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9610835,0.003910715,0.01392442,0.001821344,0.001167367,0.001174298,0.00005725826,0.001139536,0.01572152],"genre_scores_gemma":[0.9952179,0.0008331045,0.003251468,0.00007093633,0.0004666129,0.00006168991,0.000001659757,0.0000305694,0.0000660773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.309008,"threshold_uncertainty_score":0.9924718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03351640432980212,"score_gpt":0.1985866112622304,"score_spread":0.1650702069324283,"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."}}