{"id":"W4292348765","doi":"10.1109/itc-egypt55520.2022.9855753","title":"A Methodology for UAV Classification using Machine Learning and Full-Wave Electromagnetic Simulations","year":2022,"lang":"en","type":"article","venue":"2022 International Telecommunications Conference (ITC-Egypt)","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Drone; Radar; Computer science; Artificial intelligence; Range (aeronautics); Doppler radar; Radar engineering details; Radar imaging; Remote sensing; Engineering; Aerospace engineering; Geography; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005141978,0.0006219255,0.0004634563,0.0008816209,0.000360981,0.0007473165,0.0009622006,0.0008196805,0.002204342],"category_scores_gemma":[0.001245526,0.0003538902,0.0007943249,0.0005472249,0.0002733727,0.0005791255,0.0004475724,0.0008196314,0.000674744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006382968,"about_ca_system_score_gemma":0.0006762568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004705388,"about_ca_topic_score_gemma":0.00443895,"domain_scores_codex":[0.9997647,0.00004274576,0.00002062824,0.00005136511,0.00009640671,0.00002417345],"domain_scores_gemma":[0.9996346,0.0001275933,0.00004828977,0.000046281,0.0001287624,0.00001444064],"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":[0.00003968267,0.00007470563,0.001704973,0.00009881308,0.00007127319,0.00009321743,0.00005076189,0.7984322,0.006871872,0.008303436,0.001846712,0.1824124],"study_design_scores_gemma":[0.000001597064,0.000009069581,0.0001177411,0.000004756861,0.000002138722,0.00001445626,0.000003757421,0.9972019,0.0009985445,0.0009906089,0.0006530955,0.000002337796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006501379,0.00009790572,0.9910131,0.00006705874,0.00003917262,0.00007628448,0.00007433721,0.0008025218,0.001328321],"genre_scores_gemma":[0.1818622,0.0002272511,0.8130764,0.00008799133,0.00004113598,0.0004663394,0.0004080897,0.000130388,0.003700232],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004705388,"threshold_uncertainty_score":0.009356022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1372878856749364,"score_gpt":0.3606357586863778,"score_spread":0.2233478730114414,"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."}}