{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006853813,0.0004297596,0.0005441803,0.00055739,0.000269063,0.0005971208,0.0005577801,0.0007234818,0.00118068],"category_scores_gemma":[0.002677759,0.0001801492,0.0003340594,0.0005964274,0.0003718619,0.0004589131,0.0004047702,0.00065978,0.0003556428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005607468,"about_ca_system_score_gemma":0.000457503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002925534,"about_ca_topic_score_gemma":0.001924276,"domain_scores_codex":[0.9996787,0.00009442434,0.00002354412,0.00008469351,0.0000771436,0.00004142602],"domain_scores_gemma":[0.9988903,0.0007146572,0.00009648687,0.0000818082,0.0001969446,0.00001979528],"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":[0.0001104498,0.00009627477,0.001812795,0.00007863167,0.00003323786,0.00005573646,0.00005197786,0.5125692,0.005418918,0.003844329,0.001461612,0.4744669],"study_design_scores_gemma":[0.00000120161,0.00001197866,0.000172117,0.000001862664,9.584935e-7,0.000004197734,0.000002852512,0.9984634,0.0005549917,0.0006517122,0.0001333064,0.000001518877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07874975,0.0009927648,0.9164723,0.000446767,0.0001053066,0.00005770407,0.00008895366,0.0008805355,0.002206006],"genre_scores_gemma":[0.8414177,0.0002919119,0.1542452,0.0001056091,0.0001017581,0.0001184778,0.0002347016,0.00003388435,0.003450842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002925534,"threshold_uncertainty_score":0.005816996,"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."}}