{"id":"W2081986338","doi":"10.1109/iembs.2010.5627204","title":"Electrical impedance tomography reconstruction using a monotonicity approach based on a priori knowledge","year":2010,"lang":"en","type":"article","venue":"","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; CancerCare Manitoba","funders":"","keywords":"Electrical impedance tomography; Microwave imaging; Iterative reconstruction; A priori and a posteriori; Monotonic function; Computer science; Regularization (linguistics); Tomography; Breast imaging; Algorithm; Artificial intelligence; Computer vision; Mathematics; Mammography; Microwave; Physics; Optics; Telecommunications","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":[0.0001717621,0.0002621038,0.0002431576,0.0004153557,0.000109324,0.00004808628,0.0001842025,0.0002240203,0.0000409761],"category_scores_gemma":[0.00003027409,0.0002202695,0.0002220111,0.001706147,0.0000700357,0.0001199501,0.00001168903,0.0007406076,0.00001462538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000482622,"about_ca_system_score_gemma":0.00004499761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001524636,"about_ca_topic_score_gemma":0.00001592853,"domain_scores_codex":[0.9986858,0.00002781351,0.0002640171,0.0003514436,0.0001749673,0.0004958996],"domain_scores_gemma":[0.9993552,0.00007061906,0.00003346933,0.0003008922,0.00007775548,0.0001620623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001669379,0.0009188946,0.02037354,0.0001947755,0.0001406126,0.000005558314,0.0001017778,0.007751266,0.5154096,0.004566029,0.0009316668,0.4494393],"study_design_scores_gemma":[0.0003702044,0.0001068414,0.002426197,0.00001101859,0.00002072216,0.00002618728,0.000003860285,0.962032,0.03370188,0.0002326518,0.0007224634,0.0003459526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9341649,0.0001828152,0.03328221,0.00001879,0.0004687718,0.000341866,0.00000358975,0.0009031548,0.03063386],"genre_scores_gemma":[0.9648919,0.00001618896,0.03473706,0.00006705325,0.0001966398,0.00003172112,0.000002968693,0.00003302802,0.00002341778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9542807,"threshold_uncertainty_score":0.8982329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008484288952452224,"score_gpt":0.2190656053173534,"score_spread":0.2105813163649012,"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."}}