Bibliographic record
Abstract
In our introductory discussion of all-optical networks (AONs) in Section 1.5.1 we have seen that the concept of lightpath plays a key role in wavelength-routing optical networks. A lightpath is an optical point-to-point path of light that interconnects a pair of source and destination nodes, where intermediate nodes along the lightpath route the signal all-optically without undergoing OEO conversion. As each lightpath requires one wavelength on every traversed link and the number of both wavelengths and links in AONs is limited for cost and efficiency reasons, it is impossible to interconnect every pair of nodes by a dedicated lightpath. Nodes that cannot be directly connected via a lightpath may use multiple different lightpaths to exchange data. In the resultant multihop optical network, each intermediate node terminating a lightpath performs OEO conversion. As a consequence, such opaque multihop optical networks are unable to provide transparency. Also, note that the transmission capacity between node pairs connected via a lightpath is equal to the bandwidth of an entire wavelength channel. This transmission capacity is dedicated and cannot be shared by other nodes, leading to wasted bandwidth under bursty nonregular traffic. To improve the bandwidth utilization of lightpaths, electronic traffic grooming becomes necessary at each source node. To avoid the loss of transparency and the need for electronic traffic grooming of lightpath-based optical networks, a novel solution for the design of transparent mesh wavelength division multiplexing (WDM) wide area networks was proposed in Chlamtac et al. (1999b).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".