{"id":"W2804277228","doi":"10.23919/ropaces.2018.8364169","title":"Biomedical magnetic induction tomography: An inhomogeneous green's function approach","year":2018,"lang":"en","type":"article","venue":"2018 International Applied Computational Electromagnetics Society Symposium (ACES)","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Magnetic field; Tomography; Finite element method; Electromagnetic induction; Salient; Iterative reconstruction; Current (fluid); Green's function; Computer science; Function (biology); Physics; Optics; Computer vision; Artificial intelligence; Electromagnetic coil","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002040918,0.0004367854,0.0002796769,0.0002989713,0.0002817266,0.0001574449,0.0005461398,0.000332576,0.0001760858],"category_scores_gemma":[0.000003713039,0.0004492121,0.0002601271,0.001129726,0.0004255238,0.0002301608,0.00007060776,0.0004667108,0.00009782737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001892822,"about_ca_system_score_gemma":0.00008159891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003470878,"about_ca_topic_score_gemma":0.000007622667,"domain_scores_codex":[0.9971335,0.00003653346,0.0005376896,0.0006613962,0.001009221,0.0006216789],"domain_scores_gemma":[0.9989697,0.00005897553,0.0001115,0.000251042,0.0003433833,0.0002654422],"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":[0.0006612336,0.00264811,0.001004904,0.0002607791,0.001834425,0.000009216622,0.002068175,0.0418654,0.7556832,0.04811066,0.03955206,0.1063018],"study_design_scores_gemma":[0.002076098,0.004341064,0.004864903,0.00002076589,0.0002112146,0.0002006277,0.0002109363,0.9268299,0.004755988,0.03503748,0.02001224,0.001438837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7176402,0.0008803739,0.2357983,0.001229694,0.003163226,0.001192575,0.0001352034,0.002346026,0.0376144],"genre_scores_gemma":[0.967321,0.00012005,0.02848618,0.0006029987,0.00220177,0.0001250376,0.0008631191,0.00008002695,0.0001998156],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8849645,"threshold_uncertainty_score":0.999796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00530485569796928,"score_gpt":0.1947986346370844,"score_spread":0.1894937789391151,"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."}}