Modularity and economy-of-scale effects in the optimal design of mesh-restorable networks
Bibliographic record
Abstract
Most, if not all, theoretical work on the capacity design of mesh-restorable networks has been done in an integer, but non-modular, fashion. Although OC-n modules can only approximate the exact design capacities, this has been acceptable in practice where networks are rapidly growing and demand forecasts have uncertainty in any case, or in research studies where modularization only confuses the comparison of underlying theoretical effects. However, the modularity and economy-of-scale effects in near-term dense wave division multiplexing (DWDM) transmission systems may be so large (consider 40 /spl lambda/ at OC-192 on a single fiber) that we need to study and exploit these effects in the optimal capacity design problem. To do so, we introduce and compare results with two approaches for modular optimal capacity design to non-modular reference designs which are subject to conventional post-modularization. Significant shifts in the network structure, and total costs savings up to 20% are seen in the test results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".