{"id":"W4304099324","doi":"10.36227/techrxiv.21257082.v1","title":"Machine Learning for UAV Classification Employing Mechanical Control Information","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"CMC Microsystems","keywords":"Drone; Signature (topology); Computer science; Artificial intelligence; Doppler effect; Classifier (UML); Range (aeronautics); Quadcopter; Pattern recognition (psychology); Engineering; Physics; Mathematics; 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":[0.0006733134,0.0004844705,0.0005698381,0.0005605913,0.0002667606,0.0005476812,0.0005432633,0.0006986691,0.001048683],"category_scores_gemma":[0.002670631,0.0001812706,0.0003566667,0.0006108749,0.0003735368,0.0004443403,0.0004082213,0.0006678706,0.0003221201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005443948,"about_ca_system_score_gemma":0.000484088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002479766,"about_ca_topic_score_gemma":0.001743237,"domain_scores_codex":[0.9996536,0.00009830896,0.0000255163,0.0000895284,0.00008987614,0.00004324549],"domain_scores_gemma":[0.9989219,0.000677728,0.00009611328,0.00008307653,0.0002015663,0.00001968844],"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":[0.0001101623,0.0001057308,0.001928131,0.00009818788,0.00003836566,0.00005659844,0.00005504853,0.5073714,0.006641504,0.004145685,0.001268604,0.4781806],"study_design_scores_gemma":[0.000001679497,0.00001825031,0.0002183109,0.000002719126,0.000001394797,0.000006082315,0.000003656696,0.9978651,0.0008517627,0.0008482066,0.0001809129,0.000001973027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07006749,0.001012145,0.925815,0.0003319134,0.00008336462,0.00005630333,0.00007272808,0.0007011264,0.001859948],"genre_scores_gemma":[0.8337657,0.0003113328,0.1626971,0.00008715218,0.00008658189,0.0001274842,0.0002104427,0.00003106167,0.002683284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002479766,"threshold_uncertainty_score":0.004930735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02274218098740323,"score_gpt":0.236741477556477,"score_spread":0.2139992965690737,"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."}}