{"id":"W2061378008","doi":"10.1088/0967-3334/33/5/739","title":"Level-set-based reconstruction algorithm for EIT lung images: first clinical results","year":2012,"lang":"en","type":"article","venue":"Physiological Measurement","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Electrical impedance tomography; Iterative reconstruction; Voxel; Reconstruction algorithm; Data set; Algorithm; Computer science; Tomography; Artificial intelligence; Pattern recognition (psychology); Computer vision; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009992548,0.000220286,0.0003033052,0.00005072954,0.0001154748,0.00001791977,0.0001496082,0.000161348,0.00002008671],"category_scores_gemma":[0.0002078695,0.0001597073,0.000286401,0.0002148021,0.00007777926,0.00007896095,0.00001764231,0.0002286753,0.00002520399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026902,"about_ca_system_score_gemma":0.00001420033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004059993,"about_ca_topic_score_gemma":0.00000210497,"domain_scores_codex":[0.9982749,0.00007292118,0.0005061459,0.0002904482,0.0003011985,0.0005543773],"domain_scores_gemma":[0.9992401,0.0001444218,0.00006901549,0.0002081297,0.0001381623,0.0002001792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003249921,0.0007276455,0.00318667,0.0001934257,0.0002762794,0.000001150429,0.00002683707,0.001075159,0.02516807,0.00005293424,0.05103185,0.917935],"study_design_scores_gemma":[0.01269473,0.003870802,0.5131702,0.0005280352,0.0004626606,0.00002147922,0.00007104385,0.2991206,0.1103057,0.002066422,0.05437084,0.003317446],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2440857,0.008566934,0.7277753,0.00106665,0.007760704,0.004131159,0.0007967656,0.002649298,0.003167453],"genre_scores_gemma":[0.978232,0.0001136228,0.0203021,0.0001074558,0.001017394,0.0001653458,0.00003367179,0.00001933668,0.000009066392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9146175,"threshold_uncertainty_score":0.6512675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1727139301224728,"score_gpt":0.3074818287475216,"score_spread":0.1347678986250488,"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."}}