{"id":"W2009193375","doi":"10.1088/0967-3334/34/6/645","title":"Reducing computational costs in large scale 3D EIT by using a sparse Jacobian matrix with block-wise CGLS reconstruction","year":2013,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Jacobian matrix and determinant; Algorithm; Sparse matrix; Electrical impedance tomography; Conjugate gradient method; Iterative reconstruction; Matrix (chemical analysis); Computer science; Mathematics; Mathematical optimization; Artificial intelligence; Tomography","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":[0.000459214,0.0005279173,0.0004073366,0.0005024363,0.0002779711,0.0006241784,0.000537595,0.0005249733,0.001872945],"category_scores_gemma":[0.001835905,0.0003702466,0.0003973934,0.0004991978,0.0003651317,0.0007290877,0.0007480875,0.0005477985,0.0005977346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002936636,"about_ca_system_score_gemma":0.0008576845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002875205,"about_ca_topic_score_gemma":0.003936198,"domain_scores_codex":[0.9997917,0.00004952349,0.00001273187,0.00002053689,0.0001142803,0.00001119735],"domain_scores_gemma":[0.9994377,0.0002927889,0.00005804161,0.00007858963,0.0001116087,0.00002116987],"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":[0.0002254811,0.0001023659,0.001747002,0.0002534515,0.00005147989,0.0004138765,0.0002865499,0.5079879,0.1221368,0.01341929,0.002727082,0.3506489],"study_design_scores_gemma":[0.00001803869,0.00004344583,0.0003275583,0.000006564434,0.000009305806,0.0001218046,0.00002494984,0.9786695,0.01666552,0.002234984,0.001865884,0.0000124441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008072982,0.00004022735,0.9908002,0.00008323821,0.000008698413,0.00002352842,0.00001906617,0.0004410686,0.0005109846],"genre_scores_gemma":[0.08560786,0.0001279328,0.913028,0.00004316344,0.00001390599,0.00008077616,0.00009031983,0.000156685,0.0008513054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002875205,"threshold_uncertainty_score":0.00626564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0243626549550193,"score_gpt":0.2231230256165663,"score_spread":0.198760370661547,"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."}}