{"id":"W4405740152","doi":"10.1016/j.geomat.2024.100031","title":"SAR target recognition network based on frequency domain covariance matrix and Riemannian manifold","year":2024,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Higher Education Discipline Innovation Project; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Riemannian manifold; Manifold (fluid mechanics); Covariance matrix; Covariance; Domain (mathematical analysis); Matrix (chemical analysis); Mathematics; Computer science; Pattern recognition (psychology); Artificial intelligence; Pure mathematics; Mathematical analysis; Algorithm; Statistics; Engineering; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001994151,0.0001721121,0.0001527942,0.00008120672,0.00005924485,0.00008262411,0.000088574,0.00007043251,0.0001412527],"category_scores_gemma":[0.00002664506,0.000176594,0.000034745,0.0002025739,0.00002657913,0.0001548621,0.00001638017,0.000191331,0.0001913033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006519561,"about_ca_system_score_gemma":0.00001126855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003654229,"about_ca_topic_score_gemma":0.000001567674,"domain_scores_codex":[0.9991615,0.00002679123,0.000198316,0.0002096291,0.0001252229,0.0002785589],"domain_scores_gemma":[0.9995542,0.0001097931,0.00001682554,0.0002438783,0.0000144885,0.00006079155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001462782,0.0002793135,0.001548577,0.01260173,0.0006384228,0.003010102,0.002680374,0.1618576,0.02171513,0.3577958,0.3111854,0.1265413],"study_design_scores_gemma":[0.0001714788,0.00005898513,0.0002183416,0.0009503356,0.00002473097,0.00002948502,0.00001359306,0.3389994,0.0008244389,0.6424908,0.01582797,0.0003904984],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005182881,0.00194443,0.9750538,0.0006759906,0.0004076512,0.0003931105,0.00006587098,0.003453935,0.01282236],"genre_scores_gemma":[0.218343,0.00004331871,0.7810804,0.0001489085,0.0001857106,0.00003856989,0.00004142435,0.00006783305,0.00005076639],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2953574,"threshold_uncertainty_score":0.7201293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007431978899697859,"score_gpt":0.2308577756385324,"score_spread":0.2234257967388345,"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."}}