{"id":"W2098918212","doi":"10.1016/j.patcog.2012.06.014","title":"Synthetic aperture imaging using pixel labeling via energy minimization","year":2012,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer vision; Computer science; Pixel; Synthetic aperture radar; Minification; Energy (signal processing); Aperture (computer memory); Energy minimization; Image (mathematics); Mathematics; Physics","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":[0.00006158463,0.0001171539,0.00007763241,0.00007809127,0.00009407709,0.00004323344,0.00005669326,0.0000523118,0.00007617514],"category_scores_gemma":[0.00001149521,0.000127751,0.00003063442,0.000114805,0.00001290607,0.0002982118,0.00001544953,0.00007999811,0.00003968217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005131821,"about_ca_system_score_gemma":0.000003484955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001977008,"about_ca_topic_score_gemma":0.000001341112,"domain_scores_codex":[0.9994184,0.00001421603,0.0001525804,0.000111691,0.00007720233,0.0002259174],"domain_scores_gemma":[0.9997277,0.0000277517,0.00003709619,0.0001099975,0.00004765605,0.0000497755],"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":[8.936799e-7,0.00003988841,0.001242338,0.00008438611,0.00001059371,5.136305e-7,0.0001578824,0.0002075963,0.1054217,0.00000574757,0.0002079668,0.8926205],"study_design_scores_gemma":[0.0001311087,0.000003214663,0.0001902246,0.0001964445,0.00005887482,0.00006570925,0.00004064203,0.9349931,0.05831318,0.0008483856,0.004778221,0.0003808861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01875084,0.0005330174,0.9793468,0.0000605923,0.0001366902,0.00006505024,0.0000131824,0.0005171266,0.0005767607],"genre_scores_gemma":[0.972504,0.00005775724,0.02667528,0.0003107472,0.0002158489,0.000048136,0.0001326634,0.00004852694,0.000007106676],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9537531,"threshold_uncertainty_score":0.5209535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897066908070082,"score_gpt":0.234417201885801,"score_spread":0.2154465328051001,"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."}}