{"id":"W2802804377","doi":"10.1007/978-3-319-90802-1_2","title":"Energy-Efficient Partitioning Clustering Algorithm for Wireless Sensor Network","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Wireless sensor network; Cluster analysis; Computer science; Energy consumption; Reduction (mathematics); Algorithm; Wireless power transfer; Wireless; Data mining; Distributed computing; Real-time computing; Computer network; Engineering; Machine learning; Telecommunications; Electrical engineering; Mathematics","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"],"consensus_categories":[],"category_scores_codex":[0.0005016361,0.0003814683,0.0004695083,0.0001799283,0.001601265,0.0003503772,0.002114633,0.0002877822,9.412574e-7],"category_scores_gemma":[0.00002232075,0.000331248,0.0002867234,0.0003033356,0.0005007454,0.0002196964,0.001101823,0.0003038943,4.602098e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009560138,"about_ca_system_score_gemma":0.0001347067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001737325,"about_ca_topic_score_gemma":0.0000690887,"domain_scores_codex":[0.9981366,0.00001671916,0.0007759689,0.0002852632,0.0003199177,0.0004655485],"domain_scores_gemma":[0.9976628,0.0005322993,0.0006510264,0.0007639047,0.0003166638,0.00007333569],"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":[0.00000103882,0.000009690656,3.017297e-7,0.00006656307,0.00005709972,6.917361e-8,0.0004984978,0.9259732,0.000002431583,0.03482505,0.0001220826,0.03844402],"study_design_scores_gemma":[0.000201284,0.00007189096,0.000002067814,0.0003492934,0.00003863097,0.000011416,0.000002795145,0.97223,0.00007548409,0.0005527263,0.02610046,0.000363966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00006736962,0.0003048111,0.9964889,0.0003476535,0.001428877,0.0004695272,0.00004863181,0.0001287612,0.0007154555],"genre_scores_gemma":[0.002761549,0.00007329247,0.9960133,0.0002227606,0.0006807889,0.00005374117,0.00004909568,0.00003241612,0.0001130837],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04625683,"threshold_uncertainty_score":0.9999139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179523645148134,"score_gpt":0.2252166070812803,"score_spread":0.2072642425664669,"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."}}