{"id":"W4200589011","doi":"10.3390/drones5040149","title":"Convolutional Neural Networks for Classification of Drones Using Radars","year":2021,"lang":"en","type":"article","venue":"Drones","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Canada Research Chairs; University of Waterloo; University of Toronto; Defence Research and Development Canada","funders":"","keywords":"Spectrogram; Radar; Short-time Fourier transform; Computer science; Convolutional neural network; Drone; Artificial intelligence; Pulse repetition frequency; Noise (video); Pattern recognition (psychology); Time–frequency analysis; Autoregressive model; Artificial neural network; Speech recognition; Fourier transform; Telecommunications; Mathematics; Fourier analysis; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004714696,0.00007758288,0.0001172678,0.00003366636,0.0000356716,0.00000841339,0.0000603154,0.00004363885,0.000008295863],"category_scores_gemma":[0.00002571297,0.00009081648,0.00004459838,0.00009579775,0.00004304446,0.0001127477,0.00001580087,0.00005714321,3.990322e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004491016,"about_ca_system_score_gemma":0.00001307688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002622477,"about_ca_topic_score_gemma":0.000003388169,"domain_scores_codex":[0.9995305,0.00001029467,0.0001580896,0.0001037603,0.00006664942,0.0001307278],"domain_scores_gemma":[0.9996812,0.00005535277,0.00002435989,0.0001371078,0.0000823761,0.00001963631],"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":[0.00001921454,0.00004948603,0.005049304,0.0002215414,0.00007003424,0.000003859359,0.0001228192,0.4840697,0.4764166,0.01599886,0.002231122,0.01574746],"study_design_scores_gemma":[0.0001178928,0.00000580166,0.002067355,0.00002050618,0.00001268982,0.0000111458,0.00003407106,0.966974,0.02728846,0.002097261,0.001272602,0.00009823491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08523478,0.0008514359,0.9131292,0.00007594876,0.0002608006,0.00009830927,0.00001492803,0.0002455059,0.00008906527],"genre_scores_gemma":[0.9244744,0.00002625135,0.07529281,0.00001364473,0.00008779354,0.00001373867,0.00004536257,0.00002429831,0.00002169447],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8392397,"threshold_uncertainty_score":0.3703389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03233409572817,"score_gpt":0.2809832241831535,"score_spread":0.2486491284549835,"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."}}