{"id":"W2133011206","doi":"10.1109/icsnc.2006.2","title":"A Flexible Weight Based Clustering Algorithm in Mobile Ad hoc Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Cluster analysis; Computer science; Mobile ad hoc network; Overhead (engineering); Distributed computing; Wireless ad hoc network; Computer network; Cluster (spacecraft); Mobile computing; Algorithm; Wireless; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0003568278,0.0002268141,0.000249123,0.0001597348,0.00008180669,0.0001959799,0.0009103011,0.0001398232,0.0001467834],"category_scores_gemma":[0.000002617835,0.0002147623,0.00008378821,0.0009454413,0.00003608501,0.0004425191,0.0003495457,0.0002611856,0.00006447245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001123467,"about_ca_system_score_gemma":0.00006078402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006970298,"about_ca_topic_score_gemma":0.0004370323,"domain_scores_codex":[0.9979862,0.00007895767,0.0003959232,0.0006054735,0.0002742861,0.0006591431],"domain_scores_gemma":[0.998841,0.0001292884,0.00007779178,0.0008093467,0.00004276394,0.00009982923],"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":[0.000005269905,0.0001165455,0.000309788,0.000006086605,0.000003521428,0.00008634171,0.00002402488,0.4250857,0.00001549811,0.001248203,0.004544855,0.5685542],"study_design_scores_gemma":[0.0006144711,0.00007539827,0.0008933026,0.00003235068,0.000001903142,0.00001193057,0.000003909375,0.954171,0.0002639574,0.0004932718,0.04317267,0.0002658337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006120704,0.003265912,0.9866345,0.0001321765,0.0005121945,0.0003975326,7.209543e-7,0.0004815531,0.007963319],"genre_scores_gemma":[0.2411316,0.0001967322,0.7516846,0.001386815,0.0006199916,0.0005223345,0.00001982695,0.00005705662,0.004381046],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5682883,"threshold_uncertainty_score":0.8757755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007113935744345424,"score_gpt":0.2187271018822807,"score_spread":0.2116131661379352,"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."}}