{"id":"W2053039692","doi":"10.1587/elex.7.722","title":"An energy-efficient dispersion method for deployment of mobile sensor networks","year":2010,"lang":"en","type":"article","venue":"IEICE Electronics Express","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Education, Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Rake; Computer science; Energy consumption; Wireless sensor network; Position (finance); Energy (signal processing); Vertex (graph theory); Software deployment; Topology (electrical circuits); Tree (set theory); Real-time computing; Algorithm; Computer network; Engineering; Electrical engineering; Mathematics; Theoretical computer science; Graph","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":[0.0002502901,0.0005572755,0.0004306232,0.0009146756,0.0005177268,0.0003764652,0.000978295,0.0005982416,0.001154144],"category_scores_gemma":[0.0009551338,0.0003278261,0.0002822199,0.0006835034,0.0003479591,0.00089487,0.0005757295,0.0005507244,0.0006102741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004893941,"about_ca_system_score_gemma":0.0004337384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005448532,"about_ca_topic_score_gemma":0.001468572,"domain_scores_codex":[0.9996532,0.00007183276,0.00001591589,0.00005176644,0.0001863659,0.00002079931],"domain_scores_gemma":[0.9996899,0.00009164189,0.00004457347,0.0000549955,0.0001046869,0.00001428148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001537745,0.0001113035,0.000790784,0.0002807074,0.00006791895,0.0002261551,0.0002200807,0.1127706,0.1810022,0.03491028,0.004016956,0.6654492],"study_design_scores_gemma":[0.00006396302,0.0002897219,0.0009073624,0.00004991388,0.00004028614,0.0009476268,0.00008676342,0.8842105,0.0668157,0.01369035,0.03282345,0.00007436969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004950763,0.0004653881,0.9928537,0.00008733787,0.0000572287,0.00002860583,0.00001195201,0.0002465284,0.001298405],"genre_scores_gemma":[0.2694925,0.001089614,0.7216504,0.00009847274,0.0001131863,0.0002209836,0.00007582892,0.0001039944,0.007155094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001154144,"threshold_uncertainty_score":0.00386101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00530289999370633,"score_gpt":0.255257393546947,"score_spread":0.2499544935532407,"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."}}