{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003044893,0.0006589806,0.0006806915,0.0008246271,0.0007642374,0.0004937116,0.001674428,0.0006127929,0.001771299],"category_scores_gemma":[0.0008523575,0.0002935343,0.0004660564,0.001478824,0.000265736,0.001000416,0.001016532,0.0006921333,0.0007382168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000808844,"about_ca_system_score_gemma":0.0008679364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003705925,"about_ca_topic_score_gemma":0.005228583,"domain_scores_codex":[0.9997206,0.00004949253,0.00001463696,0.0000537384,0.0001303041,0.00003122652],"domain_scores_gemma":[0.9997895,0.0000427655,0.00001416462,0.00003526161,0.0001068057,0.00001150261],"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":[0.0001967287,0.0001076821,0.0004844082,0.000229077,0.00007847443,0.00005826865,0.0001519928,0.4100566,0.02128948,0.02331566,0.01621306,0.5278186],"study_design_scores_gemma":[0.00002066932,0.00005008417,0.0004681374,0.00001866993,0.00001998738,0.0001365041,0.00005484244,0.9731162,0.005811839,0.0120709,0.008212978,0.00001916431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01102952,0.001236526,0.983236,0.0001785981,0.0001417724,0.00009485635,0.0001228576,0.0005327101,0.003427173],"genre_scores_gemma":[0.151978,0.001233564,0.8370646,0.0001200996,0.00008003971,0.0002907261,0.0008331171,0.0002303512,0.008169401],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003705925,"threshold_uncertainty_score":0.007368684,"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."}}