Bibliographic record
Abstract
Abstract: Roads affect animal populations in three adverse ways. They act as barriers to movement, enhance mortality due to collisions with vehicles, and reduce the amount and quality of habitat. Putting fences along roads removes the problem of road mortality but increases the barrier effect. We studied this trade‐off through a stochastic, spatially explicit, individual‐based model of population dynamics. We investigated the conditions under which fences reduce the impact of roads on population persistence. Our results showed that a fence may or may not reduce the effect of the road on population persistence, depending on the degree of road avoidance by the animal and the probability that an animal that enters the road is killed by a vehicle. Our model predicted a lower value of traffic mortality below which a fence was always harmful and an upper value of traffic mortality above which a fence was always beneficial. Between these two values the suitability of fences depended on the degree of road avoidance. Fences were more likely to be beneficial the lower the degree of road avoidance and the higher the probability of an animal being killed on the road. We recommend the use of fences when traffic is so high that animals almost never succeed in their attempts to cross the road or the population of the species of concern is declining and high traffic mortality is known to contribute to the decline. We discourage the use of fences when population size is stable or increasing or if the animals need access to resources on both sides of the road, unless fences are used in combination with wildlife crossing structures. In many cases, the use of fences may be beneficial as an interim measure until more permanent measures are implemented.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".