Effects of vegetation structure and edge habitat on the density and distribution of white-footed mice (<i>Peromyscus leucopus</i>) in small and large forest patches
Bibliographic record
Abstract
We tested the hypothesis that if the quality or quantity of vegetation in small forest patches is greater than in large patches, then vegetation attributes may contribute to greater densities of white-footed mice (Peromyscus leucopus) in smaller patches. We trapped more mice per hectare in small (range 59 ha) than in large (range 110150 ha) patches, as has been reported by other investigators. The difference appeared to be due primarily to the disproportionately high densities of P. leucopus in the edge habitat of small patches. While the greater edge-to-interior ratio in small patches could contribute to greater overall densities of mice in small patches, we trapped the same relative amount of edge and interior habitats in each of the six study patches. The structural complexity of understory vegetation was much greater in small than in large patches and also in edge than in interior habitats across the six patches. Thus, we trapped the most mice in the most structurally complex vegetation (i.e., edges of small patches). However, while vegetation at the edge of large patches was more complex than in the interior, we did not capture relatively high densities of P. leucopus in edge habitat of large patches. Hence, understory vegetation may differentially influence the distribution of P. leucopus between edge and interior habitats in forest patches of different sizes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".