Shared Spaces: Relationships Between Human Recreation and Avian Conservation in Urban Greenspaces
Bibliographic record
Abstract
Urban greenspaces are often intended for the use of people and wildlife; however, balancing the dual priorities and needs of people and wildlife from these spaces can be challenging. In the two chapters of this thesis, I investigated the impact of human activity on avian nesting success, diversity, and community structure. In my first chapter, I assessed whether human presence in urban greenspaces influences the nest survival of common species of open- cup shrub nesting birds on the island of Montreal. We conducted a field study in the summer of 2023 to collect data on bird diversity and nest survival on the island of Montreal. Through this work, we found that human activity did not significantly influence bird nest survival. In my second chapter I assessed how the presence of trails and human activity are related to bird species richness, diversity, and composition in both formally and informally managed urban greenspaces. We found that bird communities further from trails, as well as in informal urban greenspaces, were the most diverse; however, we did not find a relationship between the number of people using trails and bird diversity. This work provides land and urban greenspace managers with integrated science advice to help them support access to nature for people while maintaining existing avian biodiversity.
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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 | 0.001 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".