{"id":"W3187487380","doi":"10.1109/icuas51884.2021.9476831","title":"Velocity estimation for UAVs using ultra wide-band system","year":2021,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Computer science; Kalman filter; Position (finance); Range (aeronautics); Noise (video); Ranging; Motion capture; Computer vision; Artificial intelligence; Simulation; Motion (physics); Engineering; Aerospace engineering; Telecommunications","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":[0.0001638469,0.0004986721,0.0003626971,0.0004022902,0.0002200408,0.0004125825,0.0003704939,0.0004174111,0.0006035839],"category_scores_gemma":[0.0004671107,0.0001833035,0.0002599301,0.0002639269,0.0001586355,0.0004187669,0.0004431266,0.0003470831,0.0003611699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002123524,"about_ca_system_score_gemma":0.0003591111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002594326,"about_ca_topic_score_gemma":0.002478051,"domain_scores_codex":[0.9998319,0.00003087501,0.000008136078,0.00004763745,0.00005966864,0.00002171496],"domain_scores_gemma":[0.999889,0.00002416383,0.00002401377,0.00001473215,0.00004032302,0.000007869004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001854708,0.00005337203,0.003704725,0.0001742396,0.00007118045,0.00023313,0.0002472026,0.4485538,0.1055206,0.006993046,0.001794808,0.4324684],"study_design_scores_gemma":[0.00001533741,0.00009669332,0.001300201,0.00001493666,0.00001474784,0.0001218875,0.00004203025,0.982402,0.01204777,0.0009805752,0.002947259,0.00001662252],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02244077,0.000184544,0.9758902,0.00004478508,0.0000375679,0.00001056329,0.00001462781,0.0003463122,0.001030605],"genre_scores_gemma":[0.7036152,0.0003207343,0.293037,0.00005882519,0.00003802073,0.00005098645,0.0001365702,0.00005033279,0.002692415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002594326,"threshold_uncertainty_score":0.005158484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01568357039164209,"score_gpt":0.2266491829089191,"score_spread":0.210965612517277,"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."}}