{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004010758,0.00015942,0.0002579029,0.0001038094,0.00006698271,0.0000158478,0.00008445682,0.00007948353,0.000005911461],"category_scores_gemma":[0.00001670444,0.0001164363,0.0001572755,0.000433651,0.00004020777,0.00002875387,0.00001836978,0.0001028305,7.168749e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006985995,"about_ca_system_score_gemma":0.000003312558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001468298,"about_ca_topic_score_gemma":0.00001397912,"domain_scores_codex":[0.9988642,0.00001343518,0.0002328402,0.0002609703,0.0002922478,0.0003363259],"domain_scores_gemma":[0.9996592,0.00002095223,0.00003392142,0.0001106395,0.00006997162,0.0001052577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001081856,0.0003302066,0.001155895,0.00006432616,0.0004633609,5.169296e-7,0.0000263002,0.008953334,0.9771476,0.0005576267,0.00002287763,0.01116975],"study_design_scores_gemma":[0.0009134689,0.0004250416,0.523766,0.000007314114,0.0003840695,0.000002642617,0.000008771235,0.4247203,0.04804802,0.001141653,0.000146477,0.0004362205],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1868606,0.0001567696,0.8122729,0.00001377722,0.00003596265,0.0003109045,0.000004829868,0.0001205162,0.0002237454],"genre_scores_gemma":[0.9849606,0.00001158359,0.01475441,0.00002776041,0.0001550887,0.00003850079,0.00003999292,0.000008810231,0.000003255564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9290996,"threshold_uncertainty_score":0.4748136,"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."}}