{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001033876,0.0007073008,0.0004221438,0.0008396207,0.0002322967,0.001692796,0.0008080666,0.001320342,0.007014436],"category_scores_gemma":[0.003498988,0.0005113661,0.0007224934,0.0007794456,0.0003237178,0.0007857838,0.000941394,0.00101527,0.002476108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003468009,"about_ca_system_score_gemma":0.0006259591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00131277,"about_ca_topic_score_gemma":0.001791618,"domain_scores_codex":[0.9996557,0.00006801616,0.00002988229,0.00006625719,0.0001562977,0.00002373435],"domain_scores_gemma":[0.9991648,0.0002935597,0.00008974683,0.0001762876,0.0002384691,0.00003721004],"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":[0.0009216901,0.0001913096,0.005895271,0.0006455298,0.0002126132,0.00103215,0.0004887216,0.3281507,0.1358051,0.01825325,0.01380028,0.4946033],"study_design_scores_gemma":[0.00004332309,0.0001079996,0.001793343,0.00005279043,0.00003800279,0.001130682,0.00008785614,0.8940069,0.0822905,0.004930648,0.01540795,0.0001099297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009565317,0.000112119,0.9855703,0.0001083844,0.00002779297,0.00005147465,0.0002332917,0.00279298,0.001538326],"genre_scores_gemma":[0.09446346,0.0001861217,0.9008295,0.0001428845,0.00001790173,0.0001247024,0.0008676089,0.001111197,0.00225666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007014436,"threshold_uncertainty_score":0.02346557,"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."}}