{"id":"W2981744156","doi":"","title":"A hybrid regularization algorithm for high contrast tomographic image reconstruction","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":true,"ca_institutions":"Carleton University","funders":"","keywords":"Piecewise; Solver; Regularization (linguistics); Inverse problem; Initialization; Algorithm; Constant function; Mathematics; Inverse; Inversion (geology); Penalty method; Applied mathematics; Mathematical optimization; Mathematical analysis; Computer science; Geometry; Artificial intelligence","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":[0.000805776,0.000553939,0.0007406101,0.0007362703,0.0003769689,0.0007670014,0.001192895,0.00120455,0.001652837],"category_scores_gemma":[0.001082372,0.0004098913,0.001017302,0.0006567191,0.0005825726,0.0007181448,0.001174534,0.00119081,0.0008004584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005460578,"about_ca_system_score_gemma":0.0010679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002051433,"about_ca_topic_score_gemma":0.002117551,"domain_scores_codex":[0.9995454,0.0001148772,0.0000221223,0.00006890199,0.0002205044,0.00002807546],"domain_scores_gemma":[0.999663,0.0001180007,0.00003878348,0.00004692587,0.0001122009,0.00002108231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001556829,0.00009860037,0.0007539868,0.0002213551,0.0001554268,0.0001837782,0.0001958015,0.5324406,0.04411753,0.05043953,0.004902729,0.366335],"study_design_scores_gemma":[0.000007271592,0.00001868117,0.00006633751,0.000005476034,0.000005232519,0.00005074442,0.000004490266,0.9940859,0.00168653,0.002162314,0.001898769,0.000008097362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001647513,0.00007043035,0.9976407,0.00005094186,0.00001142274,0.0000146297,0.00001024166,0.0001586976,0.0003954049],"genre_scores_gemma":[0.04215391,0.000138056,0.9547252,0.00007309415,0.00002446872,0.0001375559,0.0001163308,0.0001281245,0.002503213],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002051433,"threshold_uncertainty_score":0.005529284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009316795682336473,"score_gpt":0.2279879186178722,"score_spread":0.2186711229355357,"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."}}