{"id":"W2801794491","doi":"10.23919/icitst.2017.8356419","title":"intel-LEACH: An optimal framework for node selection using dynamic clustering for wireless sensor networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wireless sensor network; Computer science; Node (physics); Computer network; Key distribution in wireless sensor networks; Distributed computing; Sensor node; Cluster analysis; Wireless; Wireless network; 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":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005083978,0.0003526846,0.0003785636,0.0001296895,0.001594682,0.001195439,0.001645228,0.000374427,0.000005687841],"category_scores_gemma":[0.0001205835,0.0003568379,0.0001924008,0.0001675092,0.0001036495,0.00101097,0.0004134288,0.0003371082,0.000001932432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001854617,"about_ca_system_score_gemma":0.00005180596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000604108,"about_ca_topic_score_gemma":0.0001787738,"domain_scores_codex":[0.9974511,0.00006614765,0.0004116588,0.0009360746,0.000228628,0.0009063419],"domain_scores_gemma":[0.9974757,0.000392454,0.0003862529,0.001284626,0.0002656828,0.0001953094],"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":[0.00008133226,0.0000646682,0.0001081521,0.00002381076,0.00003016667,0.000001986154,0.0001294541,0.9587105,0.0006327597,0.01598886,0.00002804837,0.02420024],"study_design_scores_gemma":[0.0004916113,0.0001772459,0.000136046,0.0001122005,0.00002294929,0.00003103926,0.00005452326,0.9970821,0.0009369196,0.0003402686,0.000142894,0.0004722744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09932455,0.00002464288,0.8975483,0.0002421481,0.001762998,0.000577758,0.000003178475,0.0004145949,0.0001018538],"genre_scores_gemma":[0.4925851,0.000004704467,0.5067399,0.0001277192,0.000352709,0.00003991957,0.000005824889,0.00004358694,0.0001005874],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3932605,"threshold_uncertainty_score":0.9998884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03830797288804057,"score_gpt":0.3209586929469634,"score_spread":0.2826507200589228,"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."}}