Road mitigation structures reduce the number of reported wildlife‐vehicle collisions in the Bow Valley, Alberta, Canada
Bibliographic record
Abstract
Abstract Human population and economic growth have resulted in roads transecting much of the North American landscape and this has negatively affected wildlife populations by fragmenting habitat, impeding movement between populations and increasing the chance of wildlife‐vehicle collisions. A common conservation tool to counteract these effects is the incorporation of road mitigation structures (RMS, i.e., jumpouts and overpasses/underpasses/fencing) into highway systems. However, gaps remain in our knowledge on RMS efficacy due to a lack of long‐term multispecies studies that can assess temporal and species‐specific variation in use. We investigate the efficacy of the Alberta Environment and Parks and Alberta Transportation RMS on the Trans‐Canada Highway (TCH) in the Bow Valley by analyzing annual reported wildlife‐vehicle collisions over a 23‐year period and wildlife use of the underpasses over a ten‐year period. We found that the incorporation of multiple underpasses and jumpouts, along with fencing, reduced the number of reported wildlife‐vehicle collisions on the TCH. We also found that wildlife use of the RMS exhibited variation with regards to month and location. Overall, our results add to the research supporting RMS effectiveness and suggest that incorporating additional similar infrastructure has the potential to further reduce wildlife‐vehicle collisions on the TCH.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".