{"id":"W2737873585","doi":"10.3390/s17071658","title":"Surface Estimation for Microwave Imaging","year":2017,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Microwave imaging; Imaging phantom; Computer science; Microwave; Computer vision; Laser; Set (abstract data type); Noise (video); Artificial intelligence; Optics; Image (mathematics); Physics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001143564,0.0001006504,0.0001160765,0.00003457603,0.0002263511,0.0001756637,0.0001424315,0.00002180569,0.000007863204],"category_scores_gemma":[0.00005006339,0.0001075248,0.00007637232,0.00001985712,0.00003537307,0.00008404869,0.00001537003,0.00005719352,0.00005530483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002854184,"about_ca_system_score_gemma":0.000003615901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003690987,"about_ca_topic_score_gemma":0.00000800632,"domain_scores_codex":[0.9995118,0.000005785717,0.0001122674,0.0001282454,0.0000511375,0.0001906977],"domain_scores_gemma":[0.9994876,0.000025492,0.00003634328,0.0003849182,0.00002673867,0.00003891971],"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":[0.000006796151,0.00001009987,0.004053898,0.0001586866,0.0001166378,0.00001035527,0.000557742,0.5616562,0.3544165,0.00007715674,0.01286631,0.06606963],"study_design_scores_gemma":[0.0001570498,0.00000194675,0.001176848,0.00002276387,0.00002907484,0.000007497094,0.00003236879,0.9349468,0.06060899,0.0002096003,0.002653706,0.0001533787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9342237,0.0001120911,0.06084518,0.0005843174,0.0003138252,0.00008116123,0.00001288052,0.0002613543,0.003565492],"genre_scores_gemma":[0.9824021,0.000009662313,0.01684994,0.00002243133,0.00005767241,0.000002289066,0.0000098347,0.00002915939,0.0006168818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3732906,"threshold_uncertainty_score":0.4384736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243666965872461,"score_gpt":0.2494885921977467,"score_spread":0.2370519225390221,"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."}}