{"id":"W2071466598","doi":"10.1109/broadnets.2006.4374424","title":"Optimal Load Balanced Clustering in Two-Tiered Sensor Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Wireless sensor network; Heuristics; Relay; Cluster analysis; Scalability; Routing (electronic design automation); Computer network; Distributed computing; Node (physics); Integer programming; Power (physics); Algorithm; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002933468,0.0002416269,0.0002701686,0.0001239919,0.00008562716,0.0001951309,0.0007752737,0.0000947681,0.00002405374],"category_scores_gemma":[0.00001070715,0.0002344449,0.00007619523,0.0007520846,0.00004802215,0.0002931073,0.000358004,0.0002585757,0.00003281362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001758823,"about_ca_system_score_gemma":0.00004130377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006928709,"about_ca_topic_score_gemma":0.001158312,"domain_scores_codex":[0.9977723,0.00009030717,0.000414717,0.0006120927,0.0003640797,0.0007464928],"domain_scores_gemma":[0.9989603,0.0001223016,0.00009351117,0.0006631161,0.00007260402,0.00008817003],"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":[0.000009984181,0.00005379901,0.001355995,0.000002441209,0.000003416362,0.0000681899,0.00003542093,0.9909803,0.0003501942,0.004493152,0.0005171725,0.002129994],"study_design_scores_gemma":[0.0008654147,0.00002100011,0.002564373,0.00002889895,0.000001626922,0.00002355715,0.00001175752,0.9951226,0.0004247188,0.00003025437,0.000606176,0.0002996036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.126693,0.0001270088,0.8517292,0.0002217281,0.0005853926,0.0001276455,2.984278e-7,0.0004025377,0.02011317],"genre_scores_gemma":[0.8397217,0.000008046885,0.1581483,0.0002300158,0.0002490038,0.00001034352,0.000003529467,0.0000190495,0.001609981],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7130287,"threshold_uncertainty_score":0.9560387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007120277863408934,"score_gpt":0.2231585924210587,"score_spread":0.2160383145576498,"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."}}