{"id":"W3146084458","doi":"10.18280/ts.380111","title":"Design and Optimization of a Finite Element Model for Electrical Resistance Tomography of Human Lungs","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Huainan Normal University","keywords":"Finite element method; Hessian matrix; Sensitivity (control systems); Particle swarm optimization; Matrix (chemical analysis); Tomography; Boundary (topology); Computer science; Mathematics; Mathematical optimization; Algorithm; Mathematical analysis; Physics; Electronic engineering; Engineering; Applied mathematics; Materials science; Structural engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005224237,0.0005777514,0.0005991691,0.0004970903,0.0002799399,0.0005853555,0.0009619879,0.001268981,0.001442913],"category_scores_gemma":[0.0008898799,0.0005212307,0.0007531411,0.0003244788,0.0004738676,0.0005996153,0.0005537418,0.0005730857,0.0004645778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000421546,"about_ca_system_score_gemma":0.0009260124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002499473,"about_ca_topic_score_gemma":0.002000596,"domain_scores_codex":[0.9996889,0.0000851955,0.00001860481,0.00006525117,0.0001196085,0.00002245449],"domain_scores_gemma":[0.9997606,0.000106714,0.00003447417,0.00002279479,0.00006233792,0.00001320408],"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":[0.0000269868,0.00002196138,0.0004880776,0.00007624745,0.0000151976,0.00005582241,0.00004561165,0.9705271,0.01207942,0.002505512,0.0002332438,0.0139249],"study_design_scores_gemma":[0.000004393078,0.00001663125,0.00008597297,0.000006650281,0.000005086737,0.00002179897,0.000008328603,0.9973897,0.001425134,0.000301619,0.0007301767,0.000004581944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005921243,0.0001042165,0.9924231,0.00007354763,0.00001371645,0.0000337362,0.0000248212,0.0001320572,0.001273501],"genre_scores_gemma":[0.4439964,0.0006706737,0.5490079,0.0001410997,0.0000261742,0.0006693108,0.000278172,0.0001655984,0.005044686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002499473,"threshold_uncertainty_score":0.004969835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525444406984669,"score_gpt":0.2193020371135485,"score_spread":0.2040475930437018,"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."}}