Machine Learning for Regenerator Placement Based on the Features of the Optical Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With network traffic projected to increase drastically over the new few years, Elastic Optical Networks (EONs) have been brought in to be the successor of the currently used optical technologies. Many factors must be taken into consideration when deploying EONs for wide-scale use. One of which is the overall network's resource allocation. A simple, uniform distribution of regenerators is too inefficient as different locations have different regenerator requirements based on the amount of network traffic they receive. On the other hand, increasing the number of installed regenerators after initial deployment will incur a substantial cost. The ideal scenario is to accurately predict the number of regenerators that each location will need. One way to provide accurate predictions for regenerator allocation is through the use of machine learning. In order to maximize the accuracy of the prediction provided by the machine learning algorithm, it must be supplied with quality input training data. In this paper, we examine the impact that different network features can have on prediction results. We then propose a list of network features that hold significant impact in regards to predicting regenerator allocation accurately.
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.
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) | 0.000 | 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 | 0.000 | 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