{"id":"W2113719271","doi":"10.1109/icc.2010.5502170","title":"Lifetime Maximization of UWB-Based Sensor Networks for Event Detection Applications","year":2010,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Wireless sensor network; Maximization; Bottleneck; Energy consumption; Benchmark (surveying); Real-time computing; Ranging; Event (particle physics); Ultra-wideband; Computer network; Distributed computing; Mathematical optimization; Telecommunications; Engineering; Embedded system","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":[0.001501606,0.000674249,0.0005881002,0.000389037,0.0002975134,0.0006560804,0.0007144841,0.00068514,0.001123278],"category_scores_gemma":[0.003695324,0.0003427411,0.0003990392,0.0006289623,0.0005229625,0.001226205,0.0006304246,0.00052524,0.0001534939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008047743,"about_ca_system_score_gemma":0.0007031387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008178665,"about_ca_topic_score_gemma":0.0009038653,"domain_scores_codex":[0.999604,0.0002197448,0.00001106551,0.000044172,0.00008796187,0.00003306264],"domain_scores_gemma":[0.9992167,0.0005809355,0.00007764063,0.00002647907,0.00007575541,0.00002249214],"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":[0.00003769868,0.00001875691,0.0002333432,0.0001061157,0.00001902908,0.00005061139,0.0000600213,0.959242,0.003052715,0.0191566,0.0007027796,0.01732018],"study_design_scores_gemma":[0.000003572467,0.00001701634,0.00006542732,0.000007022275,0.000005661973,0.00002282008,0.00001299013,0.9922723,0.0007441271,0.00624706,0.0005986415,0.000003411825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02250187,0.001037507,0.9727746,0.0002949632,0.0000167131,0.00002891653,0.00005083485,0.00008077899,0.003213927],"genre_scores_gemma":[0.8059028,0.002948496,0.1862675,0.0001328789,0.00007501994,0.0002575479,0.0001738427,0.0001163984,0.004125576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001501606,"threshold_uncertainty_score":0.007941365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005686019407835973,"score_gpt":0.2208859896672695,"score_spread":0.2151999702594335,"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."}}