{"id":"W2910689217","doi":"10.1109/usnc-ursi.2018.8602777","title":"Energy Efficient Hybrid Clustering Approach in Wireless Sensor Network (WSN)","year":2018,"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":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Wireless sensor network; Computer science; Key distribution in wireless sensor networks; Cluster analysis; Energy consumption; Efficient energy use; Computer network; Mobile wireless sensor network; Sink (geography); Distributed computing; Wireless; Engineering; Wireless network; Telecommunications; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005385499,0.0003580465,0.0003860413,0.0002296615,0.0002322378,0.0002240518,0.001385927,0.0001248669,0.00002434967],"category_scores_gemma":[0.000008844086,0.0003324896,0.0001050867,0.001300962,0.000177652,0.0001809752,0.0008650483,0.0002362964,0.0000612355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001339637,"about_ca_system_score_gemma":0.0000519126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000184587,"about_ca_topic_score_gemma":0.0001527962,"domain_scores_codex":[0.9965382,0.0002086984,0.0005330137,0.001039809,0.0005166542,0.00116361],"domain_scores_gemma":[0.9982241,0.0001504923,0.0001378958,0.001154386,0.000124126,0.0002090393],"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":[0.00001675001,0.0001776011,0.0003042777,0.000008495191,0.00001308327,0.00004780015,0.0001684607,0.9295716,0.0001996995,0.04861479,0.001094261,0.01978322],"study_design_scores_gemma":[0.0004164914,0.00007001173,0.0002857026,0.00004556209,0.000002953029,0.00006793615,0.000023408,0.995179,0.001737414,0.00007373509,0.001670038,0.0004277492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1077147,0.00006390267,0.8585833,0.0001471124,0.00107419,0.0001168174,4.769063e-7,0.0004462419,0.03185317],"genre_scores_gemma":[0.8952616,0.00001366494,0.1024012,0.0006934124,0.0007524284,0.00002258844,0.000004986061,0.00003629886,0.0008137433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7875469,"threshold_uncertainty_score":0.9999127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.011010078138986,"score_gpt":0.2098780072635054,"score_spread":0.1988679291245194,"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."}}