{"id":"W2913892216","doi":"10.1109/access.2019.2896655","title":"Focus Measure for Synthetic Aperture Imaging Using a Deep Convolutional Network","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Basic Research Program of Shaanxi Province; Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China; University of Alberta; Nvidia","keywords":"Computer science; Measure (data warehouse); Focus (optics); Synthetic aperture radar; Convolutional neural network; Artificial intelligence; Deep learning; Pattern recognition (psychology); Data mining; Optics; Physics","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":[0.0001000918,0.0001232224,0.0001261571,0.00003683742,0.0001109825,0.0001260725,0.0002826465,0.00005609039,0.00003184954],"category_scores_gemma":[0.00001237219,0.0001229317,0.00005807919,0.000159282,0.00002173886,0.0002640168,0.00002499358,0.0001096951,0.0000148121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005941711,"about_ca_system_score_gemma":0.00002247129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009246066,"about_ca_topic_score_gemma":0.000003765818,"domain_scores_codex":[0.9993249,0.000006003751,0.0001371769,0.0001709471,0.00009890932,0.0002620352],"domain_scores_gemma":[0.9995717,0.00007434576,0.00003258537,0.0002006094,0.00008335368,0.00003739335],"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":[0.00008226322,0.0001513308,0.01370249,0.0014674,0.0002655697,0.000008760558,0.0003463516,0.6123142,0.1896337,0.009912857,0.03858341,0.1335317],"study_design_scores_gemma":[0.0001934599,0.000003822977,0.0001478082,0.0001140477,0.00002795116,0.00001689605,0.00000534988,0.9779604,0.004958272,0.006857246,0.009475412,0.0002393422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008195422,0.001350746,0.9878029,0.0001611546,0.0002864073,0.0004030746,0.00001208896,0.0004767159,0.001311515],"genre_scores_gemma":[0.9768394,0.000007025319,0.02255956,0.0001406259,0.0002533318,0.0001172045,0.000006604171,0.0000459809,0.00003025415],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.968644,"threshold_uncertainty_score":0.5013008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01862870531450707,"score_gpt":0.2740781716186722,"score_spread":0.2554494663041651,"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."}}