Constructing Service Matrices for Agile All-Optical Cores
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Bibliographic record
Abstract
A semi-analytical method based on alternate projections on a linear vector space is used to construct a service matrix from a traffic matrix, where the traffic matrix represents the bandwidth requested by the edge nodes and the service matrix represents how the bandwidth will be distributed by the core of an optical star network that operates in a Time Division Multiplexing mode. The algorithm iterates over a mathematical expression of complexity O(N^2), where N denotes the number of edge nodes. The complexity of the method is therefore O(kN^2) where k denotes the number of iterations needed to converge. With N large enough one observes that k\le\leN and hence this expression tends to O(N^2). Results show that the service matrices obtained with this projection method have very high measures of similarity to the original traffic matrix, with an average similarity greater than 95% for N \geqslant 32 . The method is robust to inadmissible/bursty traffic and yields equal or improved delay performance in the optical network compared to other allocation methods.
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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 | 0.000 | 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