Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Optical network, with its enormous data carrying capability, has become the obvious choice for today's high speed communication networks. Wavelength Division Multiplexing (WDM) technology and Traffic Grooming techniques enable us to efficiently exploit the huge bandwidth capacity of optical fibers. Wide area translucent networks use sparse placement of regenerators to overcome the physical impairments and wavelength constraints introduced by all optical (transparent) networks, and achieve a performance level close to fully switched (opaque) networks at a much lesser network cost. In this dissertation we discuss our research on several issues on the optimal design of optical networks, including optimal traffic grooming in WDM optical networks, optimal regenerator placement problem (RRP) in translucent networks, dynamic lightpath allocation and dynamic survivable lightpath allocation in translucent networks and static lightpath allocation in translucent networks. With extensive simulation experiments, we have established the effectiveness and efficiencies of our proposed algorithms.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it