Bibliographic record
Abstract
Optical burst switching (OBS) is one of the recently proposed optical switching techniques which probably received the greatest deal of attention (Chen et al., 2004). OBS may be viewed as a switching technique that combines the merits of optical circuit switching (OCS) and optical packet switching (OPS) while avoiding their respective shortcomings. The switching granularity at the burst rather than wavelength level allows for statistical multiplexing in OBS, which is not possible in OCS, while requiring a lower control overhead than OPS. More precisely, in OCS, the entire bandwidth of each lightpath is dedicated to one pair of source and destination nodes and unused bandwidth cannot be reclaimed by other nodes ready to send data. Thus, OCS does not allow for statistical multiplexing. On the other hand, in OCS networks no OEO conversion is needed at intermediate nodes. As a result, OCS networks provide all-optical circuits that are transparent in terms of bit rate, modulation scheme, and protocol. OCS is well suited for large data transmissions whose long connection holding time on the order of a few minutes, hours, days, weeks, or even months justify the involved twoway reservation overhead for setting up or releasing a lightpath, which may take a few hundred milliseconds. Since many applications require only subwavelength bandwidth and/or involve bursts that last only a few seconds or less, the coarse wavelength switching granularity of OCS becomes increasingly inefficient and impractical. Unlike OCS, OPS is able to provide a significant statistical multiplexing gain due to the fact that bandwidth is not dedicated to a single connection but may be shared by multiple data flows.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".