Bibliographic record
Abstract
In the design of survivable optical networks, the cost and complexity of wavelength assignment and conversion and wavelength-selective switching is always a dominant consideration. And yet, while nodes and single DWDM channels may fail, a pre-dominant source of unavailability is physical damage to optical cables. Thus, we have considered: If it is ultimately glass that fails, what if we just replace the glass directly? More specifically, what if p-cycles were used to rapidly, simply and efficiently provide for the direct replacement of failed fiber sections with whole replacement fibers? As long as the loss budgets are adequate, entire DWDM wavebands could be restored with no switching or manipulation of individual lightpaths. Following a substitution transient, the DWDM layer would never know the break happened. In environments where fiber switching devices are low cost, and ducts are full of dark fiber, this could provide a very low cost alternative to protect an entire DWDM transport layer (or working capacity envelope) against the single largest cause of outage. Here, we make a first proposal of considering this approach. A main motivation is to remove the complexity due to wavelength assignment and wavelength continuity constraints when configuring p-cycles in a fully transparent network context. Another objective is the overall real CAPEX and OPEX cost reductions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".