A novel heuristic for topology planning and evolution of optical mesh networks
Bibliographic record
Abstract
We consider the problem of determining an optimal facilities route topology for an optical mesh-restorable network. In general, there will be a set of existing rights-of-way for cable ducts etc. and a limited number of new acquisition prospects for topology evolution. The enhancement of network connectivity is of particular interest in a mesh-based transport network, but new rights-of-way can be very expensive. The interaction between the edge costs and capacity savings in a mesh network is also different than in prior topology design problems. We describe a three-stage heuristic based on a particular hypothesis about the problem. The heuristic runs quickly and produces solutions that typically cannot be improved upon by the optimal formulation in 6 to 12 hours and are within 8% of optimal in cases where the optimum reference could be solved. The heuristic also provides a fairly tight upper bound to help in solving the complete problem.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".