Road mitigation structures reduce the number of reported wildlife‐vehicle collisions in the Bow Valley, Alberta, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it