Bibliographic record
Abstract
Highway and municipal traffic in Canada, particularly in the major urban areas, continues to rise. In the Toronto area, Highway 401 is the major east/west corridor and has annual average daily traffic levels exceeding 400,000. While an express toll route that opened in 1996 was expected to ease traffic on Highway 401, traffic levels on the toll route are also quite high. With increasing heavy vehicle traffic, the highway infrastructure is deteriorating more rapidly than expected which has resulted in the need to renew and repair the highway infrastructure. In order to minimize the impact of the road construction on the highway users, both the highway and toll road agencies have turned to innovation in design, construction and traffic management. This has included the use of advanced warning systems for construction, provision of the same number of lanes during construction, performance incentives and penalties, use of police presence, pavement construction innovations such as fast track concrete, pre-cast concrete panels, thin surface restoration techniques such as micro-surfacing and texturization, dowel bar retrofit, foaming injection to stabilize concrete slabs, slab stitching, etc. This paper provides an overview of the innovations to minimize the impact of road construction on the traveling public and surrounding home owners and businesses.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".