Data from: Temporal road closures improve habitat quality for wildlife
Bibliographic record
Abstract
Increasing levels of human activity threaten wildlife populations through direct mortality, habitat degradation, and habitat fragmentation. Area closures can improve habitat quality for wildlife, but may be difficult to achieve where tourism or other economic drivers are a priority. Temporal closures that limit human use during specific times of day have potential to increase habitat quality for wildlife, while continuing to provide opportunities for human use. However, the effectiveness of daily temporal closures has not been tested. We assessed how implementation of a temporal road closure affected wildlife movements in Banff National Park. Parks Canada closed a popular 17 km stretch of road between 2000 and 0800 hours to improve habitat quality for wildlife. We assessed the effectiveness of the closure on nine mammal species using three sets of data: remote cameras, road surveys, and grizzly bear (Ursus arctos) GPS data. In all three analyses, wildlife detection rates on the road doubled during the closure while remaining unchanged in reference areas. Our strong and consistent results suggest temporal closures are an important conservation tool that can increase habitat quality for wildlife while minimizing effects on people.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.046 |
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".