{"id":"W2946225330","doi":"10.1088/1361-6579/ab248e","title":"Influence of reconstruction settings in electrical impedance tomography on figures of merit and physiological parameters","year":2019,"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":"Technische Universität Wien Bibliothek; Technische Universität Wien; Vienna Science and Technology Fund","keywords":"Electrical impedance tomography; Figure of merit; Iterative reconstruction; Image quality; Ground truth; Tomography; Data consistency; Computer science; Mathematics; Artificial intelligence; Image (mathematics); Computer vision; Medicine; Radiology","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":[0.005890077,0.0009024324,0.0006078563,0.0008313329,0.0001996929,0.001066705,0.0004894618,0.001059702,0.001124146],"category_scores_gemma":[0.02219544,0.0003449614,0.0006726002,0.0004945087,0.000644276,0.0007462086,0.000675211,0.0004570351,0.0002902629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003130202,"about_ca_system_score_gemma":0.0002722003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003331879,"about_ca_topic_score_gemma":0.0002263223,"domain_scores_codex":[0.9969532,0.001691867,0.0002980616,0.0004389657,0.0004876929,0.0001300505],"domain_scores_gemma":[0.9821797,0.01462616,0.00131455,0.001154563,0.0006146331,0.0001105183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.007140893,0.0006539289,0.05041261,0.001743595,0.0006353186,0.0005284203,0.0005127654,0.418794,0.3530466,0.002109132,0.0008242446,0.1635984],"study_design_scores_gemma":[0.0001189397,0.003185517,0.05419616,0.0003409671,0.0005881153,0.001082535,0.000229691,0.5821701,0.3541696,0.001763164,0.001998633,0.0001564895],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8496123,0.001765914,0.1465659,0.0001394323,0.00003820576,0.00008240536,0.0003581404,0.0004513102,0.0009863905],"genre_scores_gemma":[0.9680761,0.000285317,0.03090409,0.0000274706,0.00000788083,0.00006001904,0.0003480903,0.0001085965,0.0001824914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005890077,"threshold_uncertainty_score":0.03115004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02127966165158083,"score_gpt":0.2114142607815881,"score_spread":0.1901345991300073,"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."}}