{"id":"W4404739967","doi":"10.1109/mobisecserv63327.2024.10759963","title":"Voice Command Drone for Law Enforcement and Emergency Response","year":2024,"lang":"en","type":"article","venue":"","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Law enforcement; Drone; Computer security; Computer science; Aeronautics; Law; Business; Political science; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001269125,0.0002852075,0.0001352419,0.0001814434,0.0002647578,0.0005619164,0.0002578375,0.0004018459,0.004589274],"category_scores_gemma":[0.00031186,0.00005774101,0.0001106186,0.0001336795,0.0002229716,0.0005605876,0.0004562017,0.0003265777,0.0008030093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001959239,"about_ca_system_score_gemma":0.0002870637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001078334,"about_ca_topic_score_gemma":0.001692114,"domain_scores_codex":[0.9998728,0.0000256741,0.000004413706,0.0000195,0.00006300503,0.00001459444],"domain_scores_gemma":[0.9999181,0.00002373138,0.00001230775,0.00001155739,0.00002582772,0.000008489216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005617705,0.0002175225,0.003217015,0.0004899679,0.00005953011,0.0006155793,0.0002320728,0.169938,0.1883959,0.1007895,0.01588515,0.519598],"study_design_scores_gemma":[0.0000864202,0.0008814743,0.002657324,0.00008424866,0.00003668109,0.0006281663,0.000420355,0.8041741,0.04291641,0.0207133,0.1273492,0.00005236958],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1115078,0.00360079,0.7732696,0.00157297,0.0005347796,0.0001748544,0.0002713635,0.001246088,0.1078217],"genre_scores_gemma":[0.9318611,0.001087944,0.04401955,0.0002657365,0.00009687556,0.00007521845,0.0001606645,0.00003476986,0.02239819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004589274,"threshold_uncertainty_score":0.01535261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008348808640623628,"score_gpt":0.2404963678883105,"score_spread":0.2321475592476869,"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."}}