{"id":"W3197404650","doi":"10.1109/access.2021.3105594","title":"Target Detection Through Riemannian Geometric Approach With Application to Drone Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Radar Systems and Signal Processing","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of National Defence; Defence Research and Development Canada; Carleton University","funders":"Defence Research and Development Canada","keywords":"Riemannian geometry; Mathematics; Information geometry; Statistical manifold; Riemannian manifold; Clutter; Algorithm; Mathematical analysis; Radar; Computer science; Geometry","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":[0.0001027989,0.0001480477,0.0001730516,0.0001513984,0.0001404943,0.0002057465,0.0001502817,0.0000866246,0.000007340124],"category_scores_gemma":[0.00001117174,0.0001398661,0.00003006864,0.001764914,0.00001001817,0.0006153279,0.00001700095,0.0001447504,0.00002582454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000109986,"about_ca_system_score_gemma":0.00001661836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001239813,"about_ca_topic_score_gemma":0.000107436,"domain_scores_codex":[0.999055,0.00001995911,0.000196573,0.0002878587,0.0002233031,0.0002173167],"domain_scores_gemma":[0.9995325,0.00001529805,0.00004402641,0.000234675,0.000105253,0.0000682393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003899046,0.00007830025,0.0005507298,0.0006749902,0.0001026163,0.00001496635,0.0006276422,0.611549,0.245646,0.00001838487,0.0002565598,0.1404418],"study_design_scores_gemma":[0.0003091762,0.00005095574,0.002036281,0.00004035793,0.00002531109,0.00007548681,0.0001468644,0.09802843,0.8915339,0.0001593114,0.007222864,0.0003710983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1954074,0.0002856089,0.8008065,0.00001198967,0.0002653185,0.0002003356,0.000001842724,0.0002389286,0.002782111],"genre_scores_gemma":[0.9962533,0.000007673808,0.003131523,0.00004222479,0.0002989881,0.000134128,0.000006569338,0.0000447842,0.0000808734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8008458,"threshold_uncertainty_score":0.5703575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01291134111083823,"score_gpt":0.2358819269433639,"score_spread":0.2229705858325257,"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."}}