{"id":"W193647528","doi":"","title":"A hybrid regularization method for image reconstruction of electrical impedance tomography","year":2013,"lang":"en","type":"article","venue":"International Conference on Image Processing","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Electrical impedance tomography; Iterative reconstruction; Regularization (linguistics); Tomography; Electrical impedance; Computer science; Electrical resistivity tomography; Computer vision; Artificial intelligence; Physics; Optics; Engineering; Electrical resistivity and conductivity; Electrical engineering","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":[0.0006120212,0.0005203746,0.0005835562,0.0005235581,0.0002575346,0.0006657632,0.000873684,0.001061577,0.001805936],"category_scores_gemma":[0.001021621,0.0003596368,0.0008072646,0.0005210568,0.0003491381,0.0006495159,0.0008774194,0.0009423569,0.0006251464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000261076,"about_ca_system_score_gemma":0.0006699395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001753187,"about_ca_topic_score_gemma":0.002446197,"domain_scores_codex":[0.999749,0.00008643462,0.00001447286,0.00003149881,0.0001019638,0.00001664056],"domain_scores_gemma":[0.9996932,0.0001265894,0.00002702034,0.00003665291,0.00009530235,0.00002118292],"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":[0.0004031732,0.0002089471,0.001169403,0.000584487,0.000289833,0.0002933319,0.0002137272,0.3013442,0.1964489,0.05797569,0.00682764,0.4342407],"study_design_scores_gemma":[0.00000972324,0.00001934038,0.0001387271,0.000008588833,0.00001248262,0.00009069441,0.000006542788,0.9892975,0.006475873,0.001607347,0.002318658,0.0000144777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003663595,0.0001221764,0.9953406,0.00008753443,0.0000216662,0.00001512722,0.00002743932,0.0001337819,0.0005881269],"genre_scores_gemma":[0.08241424,0.0003502126,0.9114445,0.0001263992,0.00006279464,0.0001052043,0.0001868969,0.000233018,0.005076775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001805936,"threshold_uncertainty_score":0.006041467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01254019910907141,"score_gpt":0.2708157270237952,"score_spread":0.2582755279147238,"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."}}