{"id":"W2092992358","doi":"10.1109/tpds.2011.192","title":"Distributed Throughput Optimization for ZigBee Cluster-Tree Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Parallel and Distributed Systems","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Science Council; Intel Corporation","keywords":"Computer science; Computer network; NeuRFon; Network topology; Wireless sensor network; Throughput; Wireless; Wireless network; Bandwidth (computing); Tree (set theory); Distributed computing; Key distribution in wireless sensor networks","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"],"consensus_categories":[],"category_scores_codex":[0.000265203,0.0003787736,0.0004268411,0.0001171219,0.0005226704,0.0002408057,0.0005556388,0.0003005413,0.000009017843],"category_scores_gemma":[0.000007683288,0.0003527258,0.0001703091,0.000577145,0.0001008235,0.000430139,0.000008673549,0.0002456279,0.000007549129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009238851,"about_ca_system_score_gemma":0.0000369203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001213973,"about_ca_topic_score_gemma":0.00004139143,"domain_scores_codex":[0.9976233,0.0001579351,0.0005894045,0.0007357075,0.0002785776,0.0006150718],"domain_scores_gemma":[0.9983848,0.0002432391,0.0002237434,0.0006884349,0.0002097979,0.0002500407],"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":[0.0001105744,0.000233543,0.00001807758,0.00003210035,0.00007505463,0.00000591247,0.0001286791,0.9944586,0.000005633523,0.002587243,0.000925136,0.001419439],"study_design_scores_gemma":[0.001359393,0.0002313924,0.0000683602,0.00007344709,0.00004888449,0.00004021953,0.00007940501,0.9969231,0.00005231755,0.00005854572,0.0006612121,0.0004037485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006133603,0.0002731955,0.9954066,0.0001271332,0.001780098,0.0007821617,0.0003223205,0.0004753915,0.0002197253],"genre_scores_gemma":[0.9742767,0.0001202626,0.02474037,0.00008095393,0.00009762682,0.0002970402,0.0002246811,0.0000318914,0.0001304995],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9736633,"threshold_uncertainty_score":0.9998925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02592299096344838,"score_gpt":0.2233344299203935,"score_spread":0.1974114389569451,"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."}}