Bibliographic record
Abstract
Competition between the Cablecos and Telcos in Canada to win the broadband home is intense. Cablecos are already delivering voice services. Erosion of the Telco voice market is being evidenced. For Bell Canada, delivery of a broadcast video service is a critical component in winning the broadband home. Bell Canada is already an entrenched national satellite video service provider. Nevertheless, a wireline video delivery solution is essential to complement satellite and increase market share in major urban areas. Bell Canada is deploying a fibre-to-the-node infrastructure in existing residential areas for delivery of IP-based broadcast video content. Fibre-to-the-Node fully leverages the copper distribution plant enabling broad coverage and rapid revenue generation. Emerging VDSL2 technology delivers throughputs of 25 Mbps to the end customer over this infrastructure. However, delivery of the full set of competitive services including multiple high definition video streams and on demand content stresses this architecture. An evolution strategy is required. This paper compares the evolution alternatives including fibre-to-the-premises. Emphasis is on planning considerations enabling a cost-effective evolution to a fibre-to-the-premises architecture.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".