RESEARCH ARTICLE Effect of road density on abundance of white-footed mice
Bibliographic record
Abstract
Abstract While several studies have demonstrated that roads can act as barriers to small mammal movement, the relationship between road density and small mammal abundance has not yet been investi-gated. In southeastern Ontario, Peromyscus leucopus (white-footed mice) suffer high over-winter mortality rates, resulting in small springtime populations and frequent local extinctions. Peromyscus leucopus movement is known to be inhibited by roads, which should result in lower rates of immigration into and recolonization of habitats in landscapes with high road density. We tested two predictions: (1) Forest sites situated in landscapes with high road densities have a higher chance of P. leucopus being absent during the early spring than forest sites situated in landscapes with low road densities and (2) P. leucopus populations during the summer are smaller in forest sites situated in landscapes with high road densities than in landscapes with low road densities. We sampled P. leucopus in focal patches within nineteen landscapes (7 rural, low-road-density land-scapes; 7 rural, high-road-density landscapes; 5 urban landscapes). There was no significant relationship between road density and the presence/absence of P. leucopus during the early spring. We found a significant positive effect of road density on P. leuc-opus relative abundance during the summer, even when we excluded the urban landscapes and based the analysis on only the 14 rural landscapes. Our results suggest that any negative effect of roads on P. leucopus populations, created by their inhibition to moving across roads, is far outweighed by some positive effect of roads on P. leucopus abundance. We suggest that the two most likely explanations are that roads are positively correlated with an important as-yet-undetermined component of habitat quality, or that roads positively affect P. leucopus by negatively affecting their predators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".