{"id":"W3135825682","doi":"10.1109/itsc55140.2022.9922133","title":"Dynamic Resource Management for Providing QoS in Drone Delivery Systems","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Drone; Computer science; Quality of service; Bernoulli's principle; Markov decision process; Queueing theory; Markov process; Dynamic Bayesian network; Resource allocation; Mathematical optimization; Distributed computing; Queue; Real-time computing; Operations research; Computer network; Bayesian probability; Artificial intelligence; Engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004159682,0.0002539202,0.0002598487,0.0005574841,0.0001760555,0.0001252299,0.0004459563,0.00007080438,0.0002013666],"category_scores_gemma":[0.00000348157,0.000302291,0.00009274844,0.0003837592,0.00001926356,0.0001645376,0.00001126462,0.0002519478,0.00002793604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006658217,"about_ca_system_score_gemma":0.00003346152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001139496,"about_ca_topic_score_gemma":0.00009508586,"domain_scores_codex":[0.9977272,0.00005962305,0.0008556779,0.0004446259,0.0006331145,0.0002797726],"domain_scores_gemma":[0.9993023,0.00004486181,0.0001742394,0.0002544259,0.0001543537,0.0000698021],"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":[0.00008246797,0.00008930724,0.0001943421,0.0002285441,0.0001049551,0.000008378159,0.0006405807,0.8925424,0.0005990997,0.1034706,0.001350715,0.0006886214],"study_design_scores_gemma":[0.0005386179,0.00007965344,0.0002903445,0.0001498188,0.00002811279,0.000004322067,0.005071877,0.9609144,0.0001952842,0.00005347716,0.03235935,0.0003147132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.190605,0.0008053919,0.768299,0.0002664662,0.01298568,0.007176914,0.002142188,0.0007890785,0.01693028],"genre_scores_gemma":[0.9905832,0.0002655683,0.0004210833,0.00003735532,0.0001153744,0.004546755,0.001520868,0.00006334763,0.002446379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7999783,"threshold_uncertainty_score":0.9999429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02820465156832261,"score_gpt":0.2533804109130022,"score_spread":0.2251757593446796,"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."}}