{"id":"W1989343095","doi":"10.1155/2013/142602","title":"Analysis of Moving Object Imaging from Compressively Sensed SAR Data in the Presence of Dictionary Mismatch","year":2013,"lang":"en","type":"article","venue":"International Journal of Antennas and Propagation","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Upsampling; Computer science; Azimuth; Synthetic aperture radar; Coded aperture; Algorithm; Gaussian; Artificial intelligence; Range (aeronautics); Position (finance); Mean squared error; Computer vision; Mathematics; Image (mathematics); Physics; Optics; Materials science; Telecommunications; Statistics; Detector","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002121663,0.00006329708,0.0001637354,0.0002604126,0.00001391581,0.00003578947,0.000336576,0.00002087997,0.00001372655],"category_scores_gemma":[0.00006125358,0.00004575151,0.00004494925,0.0001579654,0.00003526242,0.0003838097,0.00005128306,0.0001053883,2.350836e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001277662,"about_ca_system_score_gemma":0.00001235654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004969724,"about_ca_topic_score_gemma":0.00001043795,"domain_scores_codex":[0.9991497,0.00005343261,0.0003822066,0.000073708,0.0002884651,0.00005247426],"domain_scores_gemma":[0.9990917,0.0001876084,0.000254275,0.0001434936,0.0003091778,0.00001377657],"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":[0.0001682029,0.000188298,0.199527,0.00004057096,0.002408033,0.00009811511,0.004996591,0.03407513,0.6949549,0.0002404612,0.001551156,0.06175153],"study_design_scores_gemma":[0.0001779078,0.00001698967,0.2199961,0.0002235773,0.0001034643,0.00002662087,0.0004498145,0.7682534,0.009922977,0.000723361,0.00005212961,0.00005366138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9795718,0.0006893689,0.019065,0.0002894895,0.0001303457,0.0000723721,0.00003580532,0.000009865113,0.0001359653],"genre_scores_gemma":[0.998096,0.0002879679,0.001491873,0.0000297199,0.00005762149,4.093396e-7,0.00003028769,0.000004705674,0.000001430695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7341783,"threshold_uncertainty_score":0.1865692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01776113096455042,"score_gpt":0.2560016403841884,"score_spread":0.238240509419638,"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."}}