{"id":"W2033384954","doi":"10.1088/0967-3334/32/7/s13","title":"Data-driven classification of ventilated lung tissues using electrical impedance tomography","year":2011,"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; Ottawa Hospital","funders":"","keywords":"Electrical impedance tomography; Tidal volume; Supine position; Region of interest; Lung; Biomedical engineering; Ventilation (architecture); Lung volumes; Nuclear medicine; Artificial intelligence; Computer science; Respiratory system; Tomography; Medicine; Physics; Radiology; Anatomy; Anesthesia; Internal medicine","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":[0.000242068,0.00020453,0.0003170922,0.0001163411,0.00005175199,0.00000885362,0.0004764671,0.0001117858,0.00005165645],"category_scores_gemma":[0.00003515754,0.0001568375,0.0001207679,0.0007839021,0.00008411232,0.0001257122,0.00006190877,0.000202285,0.000007318277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005964948,"about_ca_system_score_gemma":0.00001479831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000145367,"about_ca_topic_score_gemma":0.000002915405,"domain_scores_codex":[0.9983957,0.00006972848,0.0003956475,0.0003517643,0.0004185567,0.0003685392],"domain_scores_gemma":[0.9991912,0.00001796382,0.00009187999,0.0004583328,0.0001444906,0.0000960809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005763479,0.000303503,0.003088039,0.0000780289,0.0001679004,0.000002564986,0.00004420338,0.0002503243,0.9845643,0.0003087304,0.0005911018,0.01054364],"study_design_scores_gemma":[0.0004807776,0.0005554329,0.3611046,0.0001370492,0.0002202407,0.000005465471,0.00001880493,0.4620098,0.1724542,0.001930354,0.0003638442,0.0007193642],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903964,0.003360272,0.004705288,0.000006988442,0.0001567457,0.0003364133,0.00002932257,0.000349623,0.0006589192],"genre_scores_gemma":[0.9971647,0.0001888779,0.002507123,0.00001076362,0.00006976908,0.000016768,0.00002437067,0.0000166294,9.989694e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8121102,"threshold_uncertainty_score":0.6395646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1992404925712141,"score_gpt":0.2872004809468231,"score_spread":0.087959988375609,"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."}}