Bibliographic record
Abstract
Wavelength Switched Optical Networks (WSONs) have been designed to take advantage of all optical switching fabrics with a high level of automation and efficiency. Therein, the Wavelength Selective Switches (WSS) represent the core switching elements with a technology enabling multi-degree Reconfigurable Optical Add/Drop Multiplexers (ROADM) architectures with colorless, directionless and contentionless (CDC) switching. With the use of CDC ROADMs, WSON nodes are asymmetrical with respect to their switching capabilities. Firstly, the selection of the restricted switching capabilities plays a critical role in order to maximize, e.g., the grade of service subject to a given number of ports. Secondly, the asymmetry depends on the port size of WSS. In this paper, we propose an optimization model in order to establish the best ROADM switching connectivity to maximize the grade of service, for a given number of ports. We show that the grade of service can vary significantly, up to 30%, depending on the switching connectivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".