{"id":"W2022298556","doi":"10.1088/0967-3334/28/9/003","title":"A novel approach for EIT regularization via spatial and spectral principal component analysis","year":2007,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Electrical impedance tomography; Subspace topology; Regularization (linguistics); Inverse problem; Principal component analysis; Iterative reconstruction; Algorithm; Mathematics; Inverse; Tomography; Computer science; Applied mathematics; Mathematical optimization; Mathematical analysis; Artificial intelligence; Physics; Geometry; Optics","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":[0.00110058,0.0009758445,0.0007229056,0.0009301016,0.0006104826,0.0007081905,0.001095456,0.001004635,0.001252222],"category_scores_gemma":[0.002263807,0.0004207066,0.001226085,0.0008325498,0.0008168019,0.0009950233,0.00119018,0.00161083,0.0007810415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000339899,"about_ca_system_score_gemma":0.0009690819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001256233,"about_ca_topic_score_gemma":0.002047579,"domain_scores_codex":[0.9990464,0.0002510254,0.00004974857,0.0001525895,0.0004628335,0.00003733789],"domain_scores_gemma":[0.9991413,0.0002543947,0.00008304804,0.0001665452,0.000312918,0.00004183878],"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":[0.0001414749,0.0002244731,0.001052686,0.0003292196,0.0002716729,0.0002365244,0.0002959876,0.2145156,0.1380713,0.08299018,0.006253235,0.5556176],"study_design_scores_gemma":[0.00001022576,0.00004987499,0.0003258924,0.00001123792,0.00002243125,0.0001884153,0.00001683164,0.971617,0.01208918,0.008180074,0.007450277,0.00003857158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008341819,0.00003009151,0.9987771,0.00004129159,0.00001182471,0.00001061352,0.000008224725,0.00009558978,0.0001910776],"genre_scores_gemma":[0.01878621,0.0001266471,0.9795964,0.00004730372,0.00004012571,0.00008487009,0.00008391157,0.0001001043,0.001134338],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001256233,"threshold_uncertainty_score":0.005820513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04464081395664906,"score_gpt":0.2288001592143702,"score_spread":0.1841593452577212,"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."}}