{"id":"W3000235881","doi":"10.1109/lwc.2020.2965515","title":"Mobility-Assisted Over-the-Air Computation for Backscatter Sensor Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Precoding; Computer science; Channel (broadcasting); Benchmark (surveying); Wireless sensor network; Superposition principle; Key (lock); Wireless; Computation; Overhead (engineering); Real-time computing; Backscatter (email); Electronic engineering; Telecommunications; Computer network; Algorithm; MIMO; 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":[0.0002771874,0.00035514,0.0003001061,0.0001385833,0.0002313284,0.0003117375,0.00044177,0.0002902248,0.001018167],"category_scores_gemma":[0.00138178,0.0001323903,0.0001941367,0.0002119349,0.0004309295,0.0008366713,0.000707732,0.0005565003,0.0001756384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003415766,"about_ca_system_score_gemma":0.0004014801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002249147,"about_ca_topic_score_gemma":0.004215598,"domain_scores_codex":[0.9998261,0.00006361889,0.000006926804,0.00002055141,0.00005246565,0.00003034111],"domain_scores_gemma":[0.9995746,0.0002333323,0.00004936949,0.00006629986,0.00005251382,0.00002385909],"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":[0.0003088505,0.00004638622,0.002205331,0.00007655938,0.00003792419,0.0001607462,0.0001406036,0.8826062,0.02167396,0.02887727,0.001776063,0.06209006],"study_design_scores_gemma":[0.000004180985,0.00001921246,0.0000939657,0.000003210409,0.000001873354,0.00002071795,0.00001359362,0.9955909,0.001514196,0.002377201,0.0003582049,0.000002679112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09076574,0.0005495741,0.9036961,0.0005223434,0.00008991929,0.00002137857,0.00003401072,0.0004164505,0.003904503],"genre_scores_gemma":[0.9073264,0.0002436356,0.09115718,0.00008290161,0.0000296373,0.00002272952,0.00003024105,0.00002817064,0.00107898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002249147,"threshold_uncertainty_score":0.004472077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02410386285009042,"score_gpt":0.2450229414418806,"score_spread":0.2209190785917902,"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."}}