{"id":"W1991676281","doi":"10.1371/journal.pone.0103045","title":"Functional Validation and Comparison Framework for EIT Lung Imaging","year":2014,"lang":"es","type":"article","venue":"PLoS ONE","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Electrical impedance tomography; Algorithm; Smoothing; Reconstruction algorithm; Iterative reconstruction; Computer science; Mathematics; Tomography; Artificial intelligence; Medicine; Radiology; Computer vision","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":[0.0001343487,0.0001483491,0.0002396273,0.00009752339,0.0001096695,0.0001045659,0.00005667849,0.0000936871,0.00004338054],"category_scores_gemma":[0.0001180681,0.0001504211,0.00005879696,0.0002038904,0.00002984234,0.0001050995,0.00001812578,0.0002168693,0.00001807619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002348611,"about_ca_system_score_gemma":0.000005038101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003821204,"about_ca_topic_score_gemma":3.539071e-7,"domain_scores_codex":[0.9991126,0.00002163639,0.0002018972,0.0002089563,0.0001961929,0.0002586884],"domain_scores_gemma":[0.9993568,0.0003342772,0.00005160606,0.0001139935,0.00006537126,0.00007796037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001754683,0.001688772,0.7509416,0.004043615,0.001178341,5.715016e-7,0.0002312634,0.0007650631,0.09540174,0.08688673,0.003977955,0.05470886],"study_design_scores_gemma":[0.0008463819,0.0002229894,0.1119658,0.001053291,0.0007715359,0.000001234932,0.00001501386,0.7769542,0.08781073,0.01830813,0.001424763,0.0006258753],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6856244,0.003684167,0.3079263,0.001187422,0.0001680954,0.0003957068,0.0000220458,0.0002409762,0.0007508948],"genre_scores_gemma":[0.9773529,0.0001493352,0.02159693,0.00008454804,0.0006704885,0.00003772947,0.00003725047,0.00002744109,0.00004338831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7761892,"threshold_uncertainty_score":0.6133995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02582667364613039,"score_gpt":0.2348836698577335,"score_spread":0.2090569962116031,"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."}}