The offloading model for green base stations in hybrid energy networks with multiple objectives
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Bibliographic record
Abstract
Summary Based on green energy prediction and storage, a novel green base station (GBS) offloading model is proposed and can be employed with multiple objectives in this paper to save energy. By predicting the value of green energy collected by GBS and updating the residual energy of each GBS, we can obtain the maximum number of users that each GBS can offload theoretically. Then, the optimum number of users should be calculated in order to achieve different network performance. Eventually, under the restrictions of the maximum number of users and the optimum number of users, we can finish offloading for traditional base station in the network. Simulation results demonstrate that through the proposed GBS offloading model, we can fulfill compromise between maximizing green energy utilization and load balancing in the offloading process, and the effect of energy saving is remarkable. Copyright © 2016 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it