Bibliographic record
Abstract
Sustainable highway-engineering improvements have been developed or adopted to mitigate the unique environmental impact that highways and roads have in Canadian Rocky Mountain National Parks, which are also World Heritage Sites. Three levels of sustainable highway development are presented. The first is the reconstruction or rehabilitation of park roads. Examples are presented from several national parks in which parkways and low-volume roads were reconstructed or repaired in ways that reduced impacts. The second is the development of the passing-lane system on the Trans-Canada Highway in the Rocky Mountain National Parks. The passing-lane system has extended the design service life of the two-lane highway by approximately 15 years while maintaining an acceptable level of service. The third example is the twinning of 18.6 km of the Trans-Canada Highway, which represents a logical next step in a program of sequential twinning following the passing-lane phase. Discussion includes the ways in which highway-engineering improvements were developed to address and mitigate numerous potential project impacts that were identified during environmental assessment. Included in the environmental mitigations were a series of measures such as fencing, animal-crossing structures, and underpasses to address wildlife movement, biodiversity, and mortality. Stream, terrain, and vegetation disturbance-minimization techniques also were applied. Research has found that the mitigation measures have been effective in reducing wildlife–vehicle collisions by as much as 97 percent.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".