{"id":"W4367728426","doi":"10.1109/taes.2023.3272303","title":"Machine Learning for UAV Classification Employing Mechanical Control Information","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Computer science; Signature (topology); Artificial intelligence; Convolutional neural network; Doppler effect; Classifier (UML); Range (aeronautics); Doppler radar; Artificial neural network; Pattern recognition (psychology); Radar; Engineering; Aerospace engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006903056,0.0006228586,0.0005188548,0.0006740228,0.0002651423,0.0005792411,0.0006203502,0.0007806505,0.0009174825],"category_scores_gemma":[0.002218183,0.0002092664,0.00045319,0.0006248719,0.0003069323,0.0004935685,0.0003692947,0.0007747854,0.0003090338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006920373,"about_ca_system_score_gemma":0.0005642161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004446357,"about_ca_topic_score_gemma":0.003226262,"domain_scores_codex":[0.9996955,0.00007158209,0.00002687279,0.00009145435,0.00006426154,0.0000503751],"domain_scores_gemma":[0.9992749,0.0003871231,0.00007700609,0.00006399433,0.000180257,0.00001676306],"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":[0.000132622,0.0001225243,0.00303189,0.00008894426,0.00004940956,0.00007537825,0.00004274129,0.559059,0.006855523,0.002168973,0.001718965,0.426654],"study_design_scores_gemma":[0.000001414476,0.00001464805,0.0003332428,0.000003175589,0.000002130282,0.000005915098,0.00000398327,0.9978066,0.001193144,0.0004558538,0.0001778458,0.000002005621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.177535,0.001801112,0.8144701,0.0006163261,0.0001734531,0.0001053524,0.0002928845,0.001658988,0.003346894],"genre_scores_gemma":[0.8921598,0.0002867807,0.1046745,0.00009612584,0.00006617264,0.0001094852,0.0004640261,0.00003109967,0.002112072],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004446357,"threshold_uncertainty_score":0.008840978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01259357772594866,"score_gpt":0.2390101114403084,"score_spread":0.2264165337143598,"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."}}