<title>Capacity design studies of span-restorable mesh transport networks with shared-risk link group (SRLG) effects</title>
Bibliographic record
Abstract
We study the total capacity requirements of span-restorable mesh network designs as the percentage of all possible dual failure combinations incident on a common node is increased. Our interest is in questions such as: Are there any guidelines or insights as to how many such SRLGs can be sustained before the capacity penalty becomes severe? Can we diagnose which SRLGs are the most limiting to overall network efficiency? When would it be worthwhile to take physical measures to eliminate a certain SRLG? In addressing these questions we provide a design formulation and procedure for planning any span-restorable network for a known set of SRLGs. One finding of interest is that if all dual failure combinations incident to a common node are allowed for in the design, then nearly all other dual span failure combinations (any two spans in the network) will also be restorable. We also produce experimental results showing how total capacity depends on the relative number or frequency of co-incident SRLGs and quantify how the type of SRLG will impact design costs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".