{"id":"W2167493615","doi":"10.1109/iembs.1995.575254","title":"Electrical impedance tomography: regularized reconstruction using a variance uniformization constraint","year":2002,"lang":"en","type":"article","venue":"","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Electrical impedance tomography; Regularization (linguistics); Uniformization (probability theory); Inverse problem; Algorithm; Mathematical optimization; Applied mathematics; Tomography; Mathematics; Regularization perspectives on support vector machines; Computer science; Nonlinear system; Tikhonov regularization; Mathematical analysis; Statistics; Artificial intelligence; Physics","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":[0.001532093,0.0005829856,0.0006865809,0.0004713078,0.0002094331,0.0008161308,0.0008352916,0.001134614,0.0006815198],"category_scores_gemma":[0.004358008,0.000412425,0.0005808029,0.0004707683,0.0008021768,0.001194662,0.001299922,0.0008825353,0.0002698165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000356328,"about_ca_system_score_gemma":0.0005309167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008536014,"about_ca_topic_score_gemma":0.000955838,"domain_scores_codex":[0.9990715,0.0004099775,0.00004385949,0.0001483783,0.0002907881,0.0000355187],"domain_scores_gemma":[0.9988528,0.0006385351,0.0001432657,0.0001611546,0.0001731943,0.00003097005],"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":[0.0002983252,0.0001069112,0.001428306,0.0003497523,0.0001912252,0.0003853745,0.0002247613,0.5730087,0.1016618,0.1161264,0.003455083,0.2027634],"study_design_scores_gemma":[0.00001542239,0.00003202591,0.00021089,0.00001092883,0.00001345721,0.0001127769,0.000005840817,0.9823472,0.006906707,0.008220845,0.00210626,0.00001766911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003195529,0.000102688,0.9960478,0.00012736,0.00001371532,0.000008418177,0.00001317242,0.00006463757,0.0004266699],"genre_scores_gemma":[0.1827769,0.0006768241,0.8125663,0.0002533192,0.000159873,0.0001065221,0.0002074557,0.0001806269,0.00307214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001532093,"threshold_uncertainty_score":0.008102596,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120869190134632,"score_gpt":0.1905896761784455,"score_spread":0.1785027571649823,"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."}}