Bibliographic record
Abstract
Electricity consumption contributes to a considerably large amount of the Greenhouse Gas (GHG) emissions while Information and Communication Technologies (ICT) have a significant impact on the electricity usage. Hence, novel network and component architectures leading to energy savings as well as energy-efficient network protocols have emerged since mid-2000s. On the other hand, telecommunication networks are being designed and managed to meet the bandwidth and quality of service requirements of the Internet users. As the bandwidth requests increase tremendously, service availability emerges as a key provisioning metric, since huge data losses may occur even if the service unavailability due to a network failure lasts a short time. Survivable network design and management increases resource-overbuild due to the deployment of spare resources. Obviously, deployment of spare resources increases energy consumption while establishing robust connections. Thus, “green survivability” arises as a new paradigm constrained to the trade-off between energy consumption of the network equipments versus high reliability for the subscribers. This chapter points out the challenges and explains the recent trends in provisioning green survivability for the optical backbone and access networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".