{"id":"W1591356735","doi":"10.1109/glocom.2008.ecp.27","title":"Lifetime Analysis for Wireless Sensor Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Wireless sensor network; Computer network; Computer science; Sink (geography); Probability density function; Node (physics); Key distribution in wireless sensor networks; Topology (electrical circuits); Wireless; Wireless network; Engineering; Mathematics; Telecommunications; Geography; Statistics; Electrical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002181299,0.00022065,0.0003833403,0.0002682575,0.0003244493,0.00008471977,0.0009275944,0.0001408504,0.00003080092],"category_scores_gemma":[0.00001784785,0.0001972835,0.0003611692,0.001998862,0.00008284994,0.0002098007,0.000175007,0.0001292297,0.00002834469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003708033,"about_ca_system_score_gemma":0.0000342288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003524292,"about_ca_topic_score_gemma":0.00002758078,"domain_scores_codex":[0.9980564,0.0000656535,0.0003379489,0.0006438994,0.0003031043,0.0005930646],"domain_scores_gemma":[0.9982434,0.0003683117,0.0001140528,0.0009148013,0.0001717163,0.0001877389],"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":[0.000007318025,0.00006481344,0.00344492,0.000002016275,0.0002245518,0.00002082943,0.00008306252,0.9759453,0.00003885626,0.0158505,0.002430744,0.001887073],"study_design_scores_gemma":[0.0002764643,0.00004239688,0.002589866,0.000003159508,0.00005706811,0.00001906516,0.000006217733,0.993983,0.0003733957,0.00002059145,0.002351662,0.0002771357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04270771,0.00008833184,0.9531989,0.0003961813,0.0003246325,0.0001735425,0.000001348305,0.0005099976,0.002599376],"genre_scores_gemma":[0.8494918,0.00005215806,0.1454765,0.0006472914,0.0002267468,0.00002901506,0.00001350031,0.00001882724,0.004044244],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8077224,"threshold_uncertainty_score":0.8044987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0163322545792525,"score_gpt":0.2247327479754701,"score_spread":0.2084004933962176,"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."}}