{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001348466,0.0001736567,0.0001961791,0.0003364047,0.00005944464,0.000158225,0.0002305548,0.0000619454,0.0001118936],"category_scores_gemma":[0.00008414208,0.0001617124,0.0001215869,0.0003496233,0.0000716623,0.0006208001,0.00001352461,0.0001684114,0.000009517788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000428829,"about_ca_system_score_gemma":0.00003912071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009674392,"about_ca_topic_score_gemma":7.630152e-7,"domain_scores_codex":[0.9988769,0.0000233216,0.0003578019,0.0002558953,0.0002451431,0.0002409402],"domain_scores_gemma":[0.9989242,0.00006518408,0.000129429,0.0001046009,0.0007174211,0.00005918719],"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":[0.00003128479,0.00003968929,0.000115774,0.0000880912,0.00003857312,7.80517e-7,0.00003135603,0.00003417528,0.4751027,0.003196253,0.0003330502,0.5209882],"study_design_scores_gemma":[0.0003192215,0.00009456425,0.0004295881,0.0001169332,0.00001540082,0.00002575272,0.00001839354,0.686268,0.2772844,0.03514729,0.00007993343,0.000200517],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0452912,0.0001469607,0.9419366,0.0004139059,0.0002496202,0.0004082607,0.00002955168,0.000248214,0.01127565],"genre_scores_gemma":[0.8117325,0.00005954026,0.1877673,0.00005424008,0.0001130376,0.0001363028,0.00002839607,0.00002395708,0.00008469942],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7664413,"threshold_uncertainty_score":0.6594442,"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."}}