{"id":"W2176491727","doi":"10.1155/2015/460506","title":"A Gradient-Assisted Energy-Efficient Backpressure Scheduling Algorithm for Wireless Sensor Networks","year":2015,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Network packet; Wireless sensor network; Scheduling (production processes); Energy consumption; Efficient energy use; Computer network; Distributed computing; Throughput; Algorithm; Wireless; Real-time computing; Mathematical optimization; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004301647,0.0004065481,0.0005535644,0.0002297378,0.00009491639,0.0001687757,0.0005843017,0.0003106471,0.00001267923],"category_scores_gemma":[0.0001018385,0.0004088526,0.0003143061,0.0004316674,0.00007494659,0.0002521451,0.00007155979,0.0005489822,0.000001905922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005347298,"about_ca_system_score_gemma":0.00005573179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000426318,"about_ca_topic_score_gemma":0.000004364176,"domain_scores_codex":[0.9970919,0.00009031843,0.001209253,0.0002890856,0.0007279165,0.0005914933],"domain_scores_gemma":[0.9964697,0.0002878242,0.0006111107,0.0002323577,0.001984861,0.000414187],"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":[0.000168828,0.0001042504,0.0001614473,0.000006970346,0.0004721032,0.0001002147,0.0000375979,0.9497916,0.00005306575,0.0001579843,0.002778467,0.0461675],"study_design_scores_gemma":[0.002336571,0.00008812816,0.0001117447,0.0001853322,0.0001097463,0.0003020059,0.0001733027,0.990985,0.0001537668,0.00005080327,0.005108406,0.0003951931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009321026,0.001666145,0.9825404,0.0001322235,0.005683712,0.0002045167,0.0002110072,0.0001785401,0.00006242635],"genre_scores_gemma":[0.9030277,0.0003868718,0.09212424,0.00008712761,0.003467578,0.00002154887,0.0007269225,0.000119618,0.00003836866],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8937067,"threshold_uncertainty_score":0.9998363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01352876663038706,"score_gpt":0.238689398073649,"score_spread":0.2251606314432619,"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."}}