{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002573069,0.0001468191,0.0001728751,0.0001626654,0.000193747,0.00006831781,0.00006574225,0.00009209035,0.000001783258],"category_scores_gemma":[0.000008929096,0.0001531913,0.00005181276,0.0002336698,0.00001386593,0.0002724486,4.578244e-7,0.0003060632,0.00002852945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000174188,"about_ca_system_score_gemma":0.00001690373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000206954,"about_ca_topic_score_gemma":0.00001748634,"domain_scores_codex":[0.9991429,0.00002776123,0.0002171523,0.0001327944,0.0001182813,0.0003611351],"domain_scores_gemma":[0.9996204,0.0001128598,0.00004307108,0.0001349138,0.00004028831,0.00004847949],"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":[0.0001230402,0.00002732815,0.00005156219,0.0004176454,0.0001911551,7.679563e-7,0.0005167869,0.8636594,0.0802073,0.004330843,0.0009052559,0.04956886],"study_design_scores_gemma":[0.0006419916,0.0001414668,0.00001840086,0.00004848234,0.00002944301,0.00001089211,0.0001527584,0.9787765,0.007122799,0.000174745,0.01269627,0.0001862473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005977045,0.0002071842,0.9909764,0.0001720461,0.0002810034,0.0005624192,0.00001754224,0.001746847,0.00005952736],"genre_scores_gemma":[0.9984458,0.0005699957,0.0002752227,0.00002340063,0.00003199455,0.0004477731,0.00001579351,0.00003766147,0.0001523627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9924688,"threshold_uncertainty_score":0.6246961,"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."}}