Impacts of Urban Forest Structure on Bat Populations in Kitchener, Ontario
Bibliographic record
Abstract
Multiple factors have led to declines in North American bat populations, namely white-nose syndrome and forest habitat loss. Studies have shown that natural forest remnants in urban areas have positive impacts on bats. However, few studies have established the relative importance of structural habitat components in supporting bat populations in urban areas. This study assessed five natural areas in Kitchener, Ontario and the impacts of forest structure on bat populations therein. Forest variables were significantly different between and within natural areas (P ≤ 0.05), and bat presence/absence showed no apparent pattern with park-wide forest structure. At a smaller scale, significant correlations were found for little brown myotis (Myotis lucifugus, Le Conte) only, which showed preference for higher densities of large trees and snags, higher canopy cover, and lower groundcover (P ≤ 0.05). Experimental design, additional habitat variables, and effects of adjacent land use may have impacted the efficacy of this study in identifying crucial habitat components for other bat species. Management strategies should prioritize conservation of mature forest stands and wetlands, control of invasive species and pests and long-term monitoring of bat populations in urban natural areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".