{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002314419,0.0001163658,0.0001324073,0.0001076215,0.0001228317,0.00004445435,0.0004071171,0.0001349935,0.00001352369],"category_scores_gemma":[0.00002755141,0.0001116587,0.0000927795,0.0004667326,0.0000427225,0.0001063024,0.00004783106,0.0001300778,0.000005620495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001774752,"about_ca_system_score_gemma":0.00003267397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001391961,"about_ca_topic_score_gemma":0.0000665746,"domain_scores_codex":[0.9989653,0.00002993065,0.0002878489,0.0003236615,0.0001700444,0.0002232332],"domain_scores_gemma":[0.9986978,0.0002197409,0.0001705262,0.0005990927,0.0002375593,0.00007527758],"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":[0.000007512163,0.00008393593,0.00007141905,0.000008141589,0.000005908286,1.041797e-7,0.000009713701,0.936071,0.008481628,0.01431522,0.00006734422,0.04087808],"study_design_scores_gemma":[0.0002809835,0.00004852787,0.0002185442,0.000004862397,0.000007276349,0.000001915937,0.000002333832,0.9609618,0.03510017,0.0001415098,0.003111516,0.0001206271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003975943,0.00001359517,0.9942266,0.0002410382,0.0004206504,0.000505234,0.000001540834,0.0002281192,0.0003873226],"genre_scores_gemma":[0.7576352,0.00000213044,0.2417344,0.0001165404,0.0001462815,0.0001434345,0.00001140766,0.00001280086,0.0001977915],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7536593,"threshold_uncertainty_score":0.4553309,"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."}}