{"id":"W4304097986","doi":"10.36227/techrxiv.21257082","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; Doppler effect; Quadcopter; Classifier (UML); Artificial intelligence; Range (aeronautics); Pattern recognition (psychology); Engineering; Physics; Aerospace engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003000401,0.0002001671,0.0002404285,0.0001640869,0.0001482887,0.0001237266,0.0001520284,0.0002105913,0.000204715],"category_scores_gemma":[0.0001139122,0.0002156643,0.0001195791,0.0000837906,0.000006449779,0.0001400088,0.00006886179,0.0005501549,0.00001504066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002014553,"about_ca_system_score_gemma":0.00003017637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002772503,"about_ca_topic_score_gemma":0.000006345962,"domain_scores_codex":[0.9989203,0.00004519903,0.0004625289,0.0001657689,0.0002134186,0.0001927479],"domain_scores_gemma":[0.9994134,0.0001050583,0.0001124741,0.000221034,0.00009383581,0.00005419258],"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":[0.0000152199,0.000007363123,0.0001012255,0.0002079049,0.00004375205,1.657565e-7,0.0000718414,0.9835389,0.0002840842,0.01090369,0.000316484,0.004509316],"study_design_scores_gemma":[0.0004794955,0.00003266523,0.0001167648,0.00001643281,0.0000436579,6.912927e-7,0.00006909034,0.9760589,0.0001253611,0.0007637111,0.02205887,0.0002343372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001052317,0.00006913775,0.9943317,0.0001821315,0.0007610053,0.000742709,0.00005367183,0.0006689468,0.002138347],"genre_scores_gemma":[0.9903011,0.0001014068,0.005961979,0.0001382727,0.0001031706,0.0002935385,0.002913318,0.00005421824,0.0001330479],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9892488,"threshold_uncertainty_score":0.8794535,"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."}}