Bibliographic record
Abstract
Abstract Extensive research has been focused on constructing the next‐generation all‐optical broadband IP networks. The design goal is to avoid having any optical‐electrical‐optical conversions in a signal path. Therefore, the optical switches are to be the core part of all‐optical networks. An optical switch may cover optical cross connects and optical add‐drop multiplexers. In this article, we focus on the design of large‐scale optical cross connects (OXCs). There are passive and active OXCs. The path settings in networks are pre‐configured if passive OXCs, made with arrayed waveguide gratings (AWGs), are used. Active OXCs enable networks to set up paths dynamically. There are several new and promising technologies developed for the active OXCs. This article is focused on micro‐electromechanical systems (MEMS), liquid crystals and thermo‐bubble‐based switches. Their future deployments will definitely have a considerable impact on all‐optical IP networks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.027 |
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".