Trans-Canada Highway and Dead Man's Flats Underpass: Is Highway Mitigation Cost Effective?
Bibliographic record
Abstract
A study of a 39 kilometer section of the Trans-Canada Highway (TCH) directly east of Banff National Park in Alberta, Canada evaluated the best locations to mitigate the effect of the TCH on the local wildlife populations and provide for reductions in wildlife-vehicle collisions (WVCs). In addition, the study conducted cost-benefit analyses to show where investments in mitigation may provide a net savings to society. Lastly, the study evaluated the cost savings associated with the development of an underpass and fencing within the study area using 6 years of pre-and post-construction data. The total number of WVCs for the study section between 1998 and 2010 was 806 or an average of 62 WVCs per year. This amounts to an average cost-to-society of $640,922 per year due to motorist crashes with large wildlife, primarily ungulates. Results indicate there are ten sites where mitigation measures would address a combination of values: local and regional conservation needs, high WVC rates, land security (can’t be developed). Of the 10 mitigation emphasis sites (MES) that were identified, five had average annual costs exceeding $20,000 per year due to WVCs making each of these an excellent candidate for cost effective mitigation measures. An analysis of a wildlife underpass with fencing at a 3 km section of the TCH within the project area near Dead Man’s Flats showed that total WVCs dropped from an annual average of 11.8 pre-construction to an annual average of 2.5 WVCs post-mitigation construction. The wildlife crossings and fencing reduced the annual average cost by over 90%, from an average of $128,337 per year to a resulting $17,564 average per year.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".