{"id":"W4409356476","doi":"10.1109/taes.2025.3560256","title":"Multiview Visual and Topological Features Coordination Aggregation Framework for SAR Target Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Shanghai Aerospace Science and Technology Innovation Foundation; National Natural Science Foundation of China","keywords":"Computer science; Automatic target recognition; Artificial intelligence; Computer vision; Topology (electrical circuits); Synthetic aperture radar; Pattern recognition (psychology); Engineering; Electrical engineering","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":[0.000453731,0.000750521,0.0007616233,0.001229617,0.0002576743,0.0008326513,0.001351575,0.0004622615,0.001129388],"category_scores_gemma":[0.0006736231,0.0002875334,0.001052092,0.001517813,0.0003729778,0.001333226,0.0009765262,0.0007441672,0.0003709831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007374778,"about_ca_system_score_gemma":0.0007082367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009125602,"about_ca_topic_score_gemma":0.007447875,"domain_scores_codex":[0.9995785,0.0000539694,0.00001976039,0.0001548657,0.0001407708,0.00005207669],"domain_scores_gemma":[0.999775,0.0000321039,0.00004774175,0.00005261981,0.00007330668,0.00001925322],"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":[0.0001278636,0.00009160503,0.001705227,0.00008936643,0.0001451493,0.0001441617,0.000113219,0.5096124,0.02158546,0.01684571,0.003644619,0.4458952],"study_design_scores_gemma":[0.000002958238,0.00002451176,0.0004409302,0.000002653144,0.00001804682,0.00003019942,0.00001343279,0.9930534,0.00165869,0.003782409,0.0009651225,0.000007653004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007136632,0.0002424055,0.9912487,0.00005251233,0.00001844829,0.00002024233,0.00008316692,0.000528776,0.00066899],"genre_scores_gemma":[0.6237191,0.0006157459,0.3708389,0.0001596451,0.0001532318,0.0001426477,0.0009805971,0.0001554523,0.003234572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009125602,"threshold_uncertainty_score":0.01814497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01290505558964719,"score_gpt":0.2653391786104333,"score_spread":0.252434123020786,"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."}}