Abundance, diversity, and community structure of small mammals in forest fragments in Prince Edward Island National Park, Canada
Bibliographic record
Abstract
Anthropogenic activities in Prince Edward Island (Canada) have created a mosaic of fragmented uneven-aged forests and agricultural and pasture lands, as well as large amounts of edge habitat. Although the mammalian fauna of the province is largely composed of small mammals, no previous study has investigated how they respond to habitat fragmentation. I surveyed 14 forest fragments in Prince Edward Island National Park to assess the effects of habitat fragmentation on the abundance and diversity of small mammals. A total of 897 small mammals from 11 different species were captured during 10 231 trap-nights. The most frequently captured species were the eastern chipmunk, Tamias striatus (53.5%), and the deer mouse, Peromyscus maniculatus (24.9%). Neither species richness, total population size, nor the ShannonWiener species-diversity index (H') was significantly associated with either fragment area or perimeter length. The results also indicated no difference in species diversity between linear fragments and other-shaped fragments. The only species showing a response to edge habitat was the eastern chipmunk. We concluded that future research in Prince Edward Island National Park should assess the abilities of small mammals and their predators to use edge habitats and agricultural fields.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".