{"id":"W3205887151","doi":"10.23919/ursigass51995.2021.9560264","title":"Recovery of Prior Information for Breast Microwave Imaging Using Neural Networks","year":2021,"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":"Artificial neural network; Microwave; Microwave imaging; Permittivity; RADIUS; Computer science; Range (aeronautics); Property (philosophy); Acoustics; Electronic engineering; Materials science; Artificial intelligence; Physics; Telecommunications; Engineering; Dielectric; Optoelectronics; Computer network","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":[0.0004983409,0.0004987605,0.0003164559,0.0003377004,0.000149878,0.0002889608,0.0003472308,0.0005163328,0.0007580579],"category_scores_gemma":[0.002482926,0.0003190828,0.0002499858,0.0003330234,0.0003126456,0.0006204995,0.0004395833,0.0006540867,0.0001890095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003963925,"about_ca_system_score_gemma":0.0003524286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003779463,"about_ca_topic_score_gemma":0.005985401,"domain_scores_codex":[0.9999009,0.0000264273,0.00000470373,0.00002250046,0.00003068742,0.00001479824],"domain_scores_gemma":[0.9994919,0.0003268742,0.00005138156,0.00004242872,0.00007687063,0.00001052924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001135777,0.0000334431,0.0005607354,0.000038291,0.00002483776,0.00003849641,0.0000226831,0.897615,0.01224235,0.001564387,0.0004180488,0.08732814],"study_design_scores_gemma":[0.000001176188,0.000006035524,0.0001451092,0.000001606899,0.000001556723,0.000003718704,0.000001393234,0.9981356,0.001234078,0.00041349,0.0000542462,0.000001956542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1199326,0.0003327084,0.8778861,0.000232597,0.00001771467,0.00001842428,0.0001126175,0.0004199326,0.001047381],"genre_scores_gemma":[0.834874,0.0003649794,0.1613926,0.00008321887,0.00004099105,0.00006793111,0.0003748444,0.00005945393,0.002741974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003779463,"threshold_uncertainty_score":0.007514954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006648350309526722,"score_gpt":0.2020606409333714,"score_spread":0.1954122906238446,"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."}}