{"id":"W4387872867","doi":"10.23919/usnc-ursi54200.2023.10289297","title":"Deep-Learning Enabled Uncertainty Estimation Applied to Experimental Near-Field Microwave Imaging of 2-D Dielectric Cylinders","year":2023,"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":"","keywords":"Microwave imaging; Dropout (neural networks); Dielectric; Microwave; Inference; Cylinder; Iterative reconstruction; Solver; Bayesian inference; Computer science; Finite element method; Deep learning; Artificial intelligence; Field (mathematics); Bayesian probability; Algorithm; Materials science; Machine learning; Mathematics; Physics; Geometry; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001074792,0.0005335769,0.0003302985,0.0003989253,0.0001952148,0.0005332207,0.0006070941,0.0006437583,0.0007497725],"category_scores_gemma":[0.003064046,0.0003072786,0.000351209,0.0003550311,0.0006128043,0.0005940135,0.0007368259,0.0007457724,0.000156678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005147367,"about_ca_system_score_gemma":0.0006863869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002925587,"about_ca_topic_score_gemma":0.003585375,"domain_scores_codex":[0.9997361,0.00007285007,0.00001367571,0.00005306672,0.00009235145,0.00003185235],"domain_scores_gemma":[0.9993918,0.0003036543,0.00006230369,0.00008844178,0.000123676,0.00003012962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002288099,0.00005952781,0.0016924,0.00012803,0.0000455797,0.0001721703,0.00008469318,0.9054948,0.03938549,0.002854847,0.0007291295,0.04912452],"study_design_scores_gemma":[0.000004814545,0.00001946624,0.0003532622,0.000003674927,0.000002266008,0.00003578035,0.000007964549,0.9841293,0.01448081,0.0007562686,0.0002006465,0.000005795035],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3447635,0.0002837187,0.65019,0.0003579588,0.00003272055,0.00005728326,0.0004537211,0.00168574,0.002175389],"genre_scores_gemma":[0.8854964,0.0001108435,0.1127875,0.00006179119,0.00001036419,0.00003850566,0.0007561165,0.00009621234,0.0006422456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002925587,"threshold_uncertainty_score":0.005817115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006165274099695763,"score_gpt":0.2249042323222887,"score_spread":0.2187389582225929,"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."}}