The Effect of Road Density and Proximity on Predation Attempts on the White Footed Mouse (Peromyscus Leucopus)
Bibliographic record
Abstract
Some authors have hypothesized that observed increases in small mammal populations with increasing road density (after controlling for habitat effects) may be due to predation release.Predation, especially predation by birds, could be reduced in areas with high road density, because of negative effects of roads on predator numbers and/or hunting activity.However, there are no studies testing the relationship between road density and predation rate on small mammals.Based on the predation release hypothesis, I predicted that Peromyscus leucopus placed in sites with higher surrounding paved road density and/or closer to a paved road would experience fewer predation attempts than P. leucopus placed in sites with lower surrounding paved road density and/or farther from a paved road.Considering all predators, there was no evidence of any decrease in predation attempts in relation to paved road density, but the credible interval was wide, and the possibility of a biologically relevant increase could not be ruled out.Considering only raptorial birds there was evidence of a decrease in predation attempts with paved road density, and an increase with increasing distance from the road, as predicted.However, the number of raptors was small and this change was not observed for the more numerous specialist mammalian predators.Overall, these results provide at best weak support for the hypothesis that reduced predation, specifically by birds, causes the positive relationship between road density and small mammal abundance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".