Management of Roadside Salt Pools to Reduce Moose‐Vehicle Collisions
Bibliographic record
Abstract
ABSTRACT Wildlife‐vehicle collisions cause numerous human fatalities and injuries, and generate considerable expenses in property damage each year. Certain characteristics of the road and its surroundings are known to have an impact on collision probability. Roadside salt pools increase the risk of collision by attracting moose ( Alces alces ) to the side of the road. In the Laurentides Wildlife Reserve of Québec, Canada, roadside salt pools were drained and filled with rocks to deter moose from drinking. We surveyed 12 roadside salt pools during 3 consecutive summers (2003–2005) from mid‐May to mid‐August. Seven salt pools were managed in autumn 2004, and 5 pools were left untreated. We equipped all 12 sites with electronic apparatus that allowed us to detect moose attendance and study their behavior. We also measured physical, chemical, and environmental characteristics of these pools and other unvisited pools in order to correlate moose attendance with specific habitat criteria. We found that moose mostly attended roadside salt pools from mid‐June to mid‐July, with a decrease in August. Moose attendance was significantly correlated with visual obstruction toward the road and water availability. Management of the pools caused a decrease in mean length of time moose spent at them. Number of visits decreased significantly at night (by 90%), which was when most visits occurred, but not during the day. The proposed management practice prevented all visiting moose from drinking brackish water. These results suggest that moose should eventually lose interest in treated salt pools, therefore decreasing the risk of moose‐vehicle collisions on the road.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".