{"id":"W4283270453","doi":"10.36227/techrxiv.20085920.v1","title":"UAV Classification using Neural Networks and CAD-generated Radar Datasets","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"CMC Microsystems","keywords":"Drone; Radar; Convolutional neural network; Computer science; Artificial intelligence; Range (aeronautics); Software; Artificial neural network; Signature (topology); Pattern recognition (psychology); Doppler radar; Remote sensing; Data mining; Geography; Engineering; Telecommunications; Mathematics","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":[0.0003230916,0.0008264005,0.0003426061,0.001564928,0.0001991658,0.0005925513,0.0005608203,0.0006285249,0.001002035],"category_scores_gemma":[0.00117437,0.0002605402,0.0005129451,0.0008044027,0.0002137844,0.0004628927,0.0003159031,0.0004513679,0.0003979425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007466691,"about_ca_system_score_gemma":0.0002495026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007568483,"about_ca_topic_score_gemma":0.00703675,"domain_scores_codex":[0.9997655,0.00003419725,0.00001741572,0.00006869674,0.00007566181,0.0000385296],"domain_scores_gemma":[0.9995927,0.0001293881,0.0000628139,0.00006619447,0.000129687,0.000019255],"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":[0.0002732577,0.0002968411,0.01051562,0.0001066493,0.00009170032,0.0003218439,0.00004192917,0.7764537,0.01261886,0.0006268433,0.003525245,0.1951276],"study_design_scores_gemma":[0.000004757249,0.00003278054,0.002578265,0.000005104261,0.000004764732,0.00002231562,0.00001570949,0.9936908,0.003160968,0.0001497503,0.0003293161,0.000005412506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8626329,0.0004660258,0.125733,0.0002784474,0.0001939476,0.0001772135,0.002180603,0.003490003,0.004847915],"genre_scores_gemma":[0.9421461,0.0001713454,0.05073276,0.0000496282,0.00002353901,0.00008963643,0.005005895,0.00004416735,0.001736773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007568483,"threshold_uncertainty_score":0.01504886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04450851825280135,"score_gpt":0.2989726912042366,"score_spread":0.2544641729514353,"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."}}