{"id":"W3047953999","doi":"10.48550/arxiv.2008.03162","title":"Deep Q-Network Based Dynamic Movement Strategy in a UAV-Assisted Network","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Base station; Real-time computing; Quality of service; Path loss; Path (computing); Wireless network; Wireless; User equipment; Computer network; Telecommunications","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":[0.0007751882,0.000598255,0.0007921921,0.0003816687,0.0005490279,0.0005533234,0.001338724,0.0007645463,0.001224422],"category_scores_gemma":[0.001241622,0.0002868965,0.0002682019,0.0004348792,0.0008165619,0.001030623,0.001050292,0.0005611299,0.0001371716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131254,"about_ca_system_score_gemma":0.0009534141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007765965,"about_ca_topic_score_gemma":0.006557247,"domain_scores_codex":[0.9997315,0.00007833499,0.0000113079,0.00006975429,0.00003769161,0.00007146966],"domain_scores_gemma":[0.9994791,0.0002308489,0.00007594829,0.00002845718,0.0001192814,0.00006641178],"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":[0.00008654066,0.00005982098,0.0007776621,0.0000350531,0.00002047165,0.00008337827,0.00006785215,0.9514638,0.003324859,0.009108259,0.001062106,0.03391013],"study_design_scores_gemma":[0.000005770367,0.00002243467,0.00005561247,0.000001452581,0.000002713883,0.000008626996,0.000007381158,0.9981686,0.0001398432,0.001442112,0.0001435933,0.000001782702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06188355,0.0002846901,0.9345485,0.0003773059,0.00005623363,0.00006092518,0.00003851969,0.0001242379,0.002626111],"genre_scores_gemma":[0.9283688,0.0001664861,0.06828507,0.0002089086,0.00002987558,0.0000749532,0.00004930331,0.00001925141,0.002797344],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007765965,"threshold_uncertainty_score":0.01544154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03382659926353422,"score_gpt":0.1653399900316693,"score_spread":0.1315133907681351,"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."}}