{"id":"W2117481040","doi":"10.1109/ccece.2006.277358","title":"Comparison of Clustering Algorithms and Protocols for Wireless Sensor Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Cluster analysis; Computer science; Wireless sensor network; Default gateway; Base station; Energy consumption; Computer network; Task (project management); Wireless; Distributed computing; Data transmission; Process (computing); Transmission (telecommunications); Machine learning; Engineering; Telecommunications","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":[0.00415918,0.0009985266,0.001073264,0.003341052,0.0009055553,0.001924564,0.00205244,0.001621914,0.00214926],"category_scores_gemma":[0.01292301,0.0003762858,0.0007720646,0.007534511,0.0005547091,0.003488392,0.0008620478,0.0009309358,0.0008391556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001494254,"about_ca_system_score_gemma":0.00131974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001123226,"about_ca_topic_score_gemma":0.001069504,"domain_scores_codex":[0.9933153,0.00191757,0.0005959118,0.000458701,0.00347471,0.0002377729],"domain_scores_gemma":[0.9944666,0.002630534,0.0003047019,0.0006193239,0.00188113,0.00009772892],"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":[0.0008841197,0.0002524689,0.001825381,0.003424461,0.0004331513,0.0001243208,0.0002725468,0.113134,0.007493315,0.05914484,0.0156978,0.7973137],"study_design_scores_gemma":[0.0003598025,0.003137641,0.009512336,0.001706716,0.0008386507,0.002549074,0.001161139,0.4391929,0.02614034,0.1082034,0.4068523,0.0003456638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08066441,0.1851096,0.6754901,0.002051198,0.00387418,0.001575736,0.001384707,0.002374567,0.04747548],"genre_scores_gemma":[0.2602666,0.112138,0.6104293,0.0007747263,0.0009815553,0.001520644,0.003382449,0.0007055006,0.009801204],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00415918,"threshold_uncertainty_score":0.02199614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02765431915019162,"score_gpt":0.3077106605673834,"score_spread":0.2800563414171918,"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."}}