{"id":"W2606708932","doi":"10.1049/joe.2016.0207","title":"De‐noising algorithm for enhancing microwave imaging","year":2017,"lang":"en","type":"article","venue":"The Journal of Engineering","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Noise (video); Algorithm; Hilbert–Huang transform; SIGNAL (programming language); Component (thermodynamics); Microwave; Computer science; Microwave imaging; Image (mathematics); Artificial intelligence; Physics; White noise; 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":[0.0007562465,0.0007480875,0.0005574176,0.0005454217,0.0003506898,0.0008672597,0.001028925,0.0007554186,0.003048836],"category_scores_gemma":[0.001890953,0.0003214929,0.0005147922,0.0006187022,0.0005184895,0.0009543656,0.000870715,0.001150274,0.001599568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004412535,"about_ca_system_score_gemma":0.000702651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008497754,"about_ca_topic_score_gemma":0.001424766,"domain_scores_codex":[0.9995725,0.00006815191,0.00002549491,0.00008104932,0.0002212601,0.00003155415],"domain_scores_gemma":[0.9993209,0.0001997558,0.00005870481,0.0001156152,0.000283628,0.0000213547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002267657,0.00008130581,0.0004895164,0.0001890824,0.00005600525,0.0001132511,0.0001597015,0.1150716,0.1387178,0.03901844,0.003875387,0.7020012],"study_design_scores_gemma":[0.00002501684,0.00009218558,0.0005600795,0.00002041675,0.00002554614,0.0002963819,0.00003004799,0.9123961,0.05394812,0.009477786,0.02309677,0.00003152074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001726369,0.00007591254,0.9972543,0.00003180817,0.00001910135,0.00001725211,0.00001261811,0.000138462,0.0007241723],"genre_scores_gemma":[0.02740352,0.0002136055,0.9679262,0.00006369213,0.0000313931,0.00008609068,0.000121971,0.00007599584,0.004077469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003048836,"threshold_uncertainty_score":0.01019937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006639348019795125,"score_gpt":0.2217113710614012,"score_spread":0.2150720230416061,"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."}}