{"id":"W2155361910","doi":"10.1109/sensorcomm.2008.86","title":"Energy Efficient Selection of Computing Elements in Wireless Sensor Networks","year":2008,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute of Steel Construction","keywords":"Computer science; Wireless sensor network; Energy consumption; Node (physics); Key distribution in wireless sensor networks; Sensor node; Wireless; Efficient energy use; Computation; Data transmission; Data processing; Wireless network; Embedded system; Computer network; Distributed computing; Telecommunications; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007835747,0.000448102,0.000409947,0.0005661562,0.0003869269,0.0005199061,0.000663618,0.0003057084,0.0004210295],"category_scores_gemma":[0.004733866,0.0003160353,0.0001385607,0.0005898409,0.0003999374,0.001229635,0.0004187073,0.0003064957,0.0002397189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002757296,"about_ca_system_score_gemma":0.000384931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003852884,"about_ca_topic_score_gemma":0.00109634,"domain_scores_codex":[0.9992622,0.0002909662,0.00004178418,0.00009886341,0.0002525587,0.00005370847],"domain_scores_gemma":[0.9985504,0.0008850993,0.0001441564,0.0001644255,0.0002193592,0.000036537],"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":[0.0005822793,0.0002037493,0.007738801,0.0002745315,0.00005316979,0.0002367678,0.0002007932,0.5056288,0.2073255,0.0195569,0.00114143,0.2570572],"study_design_scores_gemma":[0.0000331563,0.0003237346,0.002440036,0.00002611019,0.00003019877,0.0001581577,0.00009235615,0.8664649,0.1122369,0.01414819,0.00401497,0.0000313644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3248729,0.001167474,0.6691074,0.0002400483,0.00005287751,0.0001370546,0.00004982783,0.0005415537,0.003830838],"genre_scores_gemma":[0.8446046,0.0004767927,0.1536306,0.00006058128,0.0000216676,0.0001182724,0.00005130955,0.0000839385,0.0009523132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007835747,"threshold_uncertainty_score":0.004144013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01088878444745332,"score_gpt":0.2151368178140379,"score_spread":0.2042480333665846,"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."}}