Wide Area Telecommunication Network Design: Application to the Alberta Supernet
Bibliographic record
Abstract
As Internet technologies for long-distance transmission fall in price, and the volume of data transmission surpasses that of voice transmission, networks developed solely for the Internet are becoming the trend. However, long-distance Internet technologies add considerable delay to the signal and reduce its effectiveness for real-time communication, in particular, video-conferencing and broadband entertainment in wide area network design. Consequently, a mixture of technologies is necessary in designing a wide area Internet network that allows real-time communication.For various reasons, existing operations research models used in telecommunication are inadequate for our network design problem, so we propose new models and solution algorithms that allow for specific characteristics of network design. Our models extend those in literature by accounting for constraints on the distance between signal regenerators, and for the costs of sheltering this equipment.We used the Alberta SuperNet project as benchmark for wide area Internet network design for our models. The Alberta SuperNet is a cooperation between the Alberta Provincial Government and the private sector, lead by Bell West Inc. Bell West designed a high-speed Internet network that connects 422 communities in Alberta through Gigabit Ethernet optical fibre connections. The province is the first in Canada to provide this service to smaller communities.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".