{"id":"W2112908310","doi":"10.1088/0967-3334/28/7/s02","title":"Accounting for hardware imperfections in EIT image reconstruction algorithms","year":2007,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Electrical impedance tomography; Algorithm; Iterative reconstruction; Reconstruction algorithm; Computer science; Computer vision; Imaging phantom; Set (abstract data type); Artificial intelligence; Electrical impedance; Engineering; Optics; Physics; Electrical engineering","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":[0.003275718,0.001124741,0.0007777624,0.0008020457,0.0004635922,0.001750359,0.001481474,0.001319665,0.001580772],"category_scores_gemma":[0.01705699,0.0007623099,0.000743469,0.0008072893,0.0007100804,0.001658626,0.001130982,0.001156054,0.0005829937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006183343,"about_ca_system_score_gemma":0.001136152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002431526,"about_ca_topic_score_gemma":0.002583676,"domain_scores_codex":[0.9984477,0.0003920044,0.0001744458,0.0001857005,0.0007026674,0.00009734497],"domain_scores_gemma":[0.9923326,0.004301936,0.000767752,0.001352638,0.00115266,0.00009243118],"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":[0.0006785967,0.0001252981,0.008056002,0.0004378054,0.0001706956,0.000503335,0.0005932365,0.4912327,0.06699928,0.01022738,0.001241443,0.4197342],"study_design_scores_gemma":[0.00003015886,0.0001298476,0.001796427,0.00002820258,0.00007528631,0.0006758849,0.00005352761,0.9440697,0.04798852,0.002364499,0.002744226,0.00004370596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01121117,0.0001026577,0.9877857,0.00005583448,0.00001842058,0.00003073681,0.00001124202,0.0006197884,0.0001645161],"genre_scores_gemma":[0.1229296,0.0002107516,0.8758289,0.00004610788,0.00001424028,0.00007040247,0.00008661906,0.0002038796,0.0006095636],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003275718,"threshold_uncertainty_score":0.01732391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03859979908649405,"score_gpt":0.2495249008617926,"score_spread":0.2109251017752985,"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."}}