{"id":"W2029981818","doi":"10.1109/antem.2014.6887661","title":"Use of synthesized fields in microwave tomography inversion","year":2014,"lang":"en","type":"article","venue":"","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inversion (geology); Tomography; Scattering; Microwave imaging; Omnidirectional antenna; Microwave; Optics; Iterative reconstruction; Inverse scattering problem; Computer science; Inverse transform sampling; Physics; Geology; Computer vision; Telecommunications; Surface wave; Seismology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008750195,0.000604493,0.0003110316,0.0005060982,0.000178374,0.0008623974,0.0004659957,0.0004278398,0.00194322],"category_scores_gemma":[0.003279149,0.0003222082,0.0003167203,0.0004212298,0.0005233674,0.001030153,0.0006410274,0.0005569158,0.000408233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004286369,"about_ca_system_score_gemma":0.0008671284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009733203,"about_ca_topic_score_gemma":0.001886475,"domain_scores_codex":[0.9997707,0.00007626146,0.00001248927,0.00003806089,0.00008146655,0.00002094348],"domain_scores_gemma":[0.9991763,0.0004628026,0.00005723053,0.00012277,0.0001471582,0.00003378785],"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":[0.0006233476,0.0001483605,0.001954777,0.0003772533,0.0000957859,0.0002785368,0.000246881,0.314062,0.4351861,0.05297221,0.00117773,0.192877],"study_design_scores_gemma":[0.00005402608,0.0001379781,0.0005440734,0.0000337212,0.00003553109,0.0002084261,0.00005517483,0.7702097,0.2145375,0.008854631,0.005291897,0.00003727887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03490721,0.00008877232,0.9620922,0.00009831056,0.00003162673,0.00005294876,0.0001245132,0.0004508751,0.002153448],"genre_scores_gemma":[0.3033781,0.0002377891,0.6940613,0.000084764,0.00002226687,0.0000852631,0.0004292766,0.0001546913,0.001546414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00194322,"threshold_uncertainty_score":0.006500781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111924142835726,"score_gpt":0.1825112946189159,"score_spread":0.1713920531905587,"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."}}