Banding in optical add-drop multiplexers in WDM networks: preserving agility while minimizing cost
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Bibliographic record
Abstract
In this paper, we examine the use of limited tunability in reconfigurable optical add-drop multiplexers (L-ROADM). L-ROADMs can add or drop from only a subset of adjacent wavelengths on the network and are less costly than fully-reconfigurable optical add-drop multiplexers (F-ROADMs). We quantify the trade-off between tuning range and the number of F-ROADMs that can be replaced by L-ROADMs without sacrificing the set of connections that can be established. For the limited-add and drop case, an analytical solution for the band size is found. For the limited-add or limited-drop case, a nearly linear relationship was found between the size of the band and the number of L-ROADMs required, and the number of additional wavelengths required never exceeded 20%. For example, if half of the nodes in the ring were equipped with L-ROADMs that operated on 50% of the total spectrum, full connectivity could still be achieved by employing as few as 7% extra wavelengths.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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