Bibliographic record
Abstract
With the exponential growth of the number of Internet nodes, the volume of the data transported on the backbone has increased with the same trend. The load of the global Internet backbone will soon increase to tens of terabits per second. This indicates that the backbone bandwidth requirements will increase by a factor of 50 to 100 every seven years. Transportation of such high volumes of data requires suitable media with low loss and high bandwidth. Among the available transmission media, optical fibers achieve the best performance in terms of loss and bandwidth. High-speed data can be transported over hundreds of kilometers of single-mode fiber without significant loss in signal integrity. These fibers progressively benefit from reduction of cost and improvement of perf- mance. Meanwhile, the electronic interfaces used in an optical network are not capable of exploiting the ultimate bandwidth of the fiber, limiting the throughput of the network. Different solutions at both the system and the circuit levels have been proposed to increase the data rate of the backbone. System-level solutions are based on the utilization of wave-division multiplexing (WDM), using different colors of light to transmit s- eral sequences simultaneously. In parallel with that, a great deal of effort has been put into increasing the operating rate of the electronic transceivers using highly-developed fabrication processes and novel c- cuit techniques
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.061 |
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".