A Social-Ecological Analysis of Urban Tree Vulnerability for Publicly Owned Trees in a Residential Neighborhood
Bibliographic record
Abstract
The urban forest is a valuable ecosystem service provider, yet cities are frequently degraded environments with a myriad of stressors and disturbances affecting trees. Vulnerability science is increasingly used to explore issues of sustainability in complex social-ecological systems, and can be a useful approach for assessing urban forests. The purpose of this study was to identify and explore drivers of urban forest vulnerability in a residential neighborhood. Based on a recently published framework of urban forest vulnerability, a series of indicators of exposure, sensitivity, and adaptive capacity that describe the built environment, urban forest structure, and human population, respectively, were assessed for 806 trees in Toronto, Ontario, Canada. Tree mortality, condition, and diameter growth rates were then assessed using an existing 2007/2008 inventory. A bivariate analysis was first conducted to test for significant relationships of vulnerability indicators with mortality, condition, and growth. A multivariate analysis was then conducted using multiple linear regression for the continuous condition and growth variables and a multilayer perceptron neural network for the binary mortality variable. Commercial land uses and commercial buildings adjacent to trees consistently explained higher mortality rates and poor tree conditions. However, at finer spatial scales it is important to differentiate between different causes and correlates of urban forest decline within commercial land uses. Tree species, size, and condition were also important indicators of vulnerability. Understanding the causes of urban forest change and decline are essential for developing planning strategies to reduce long-term system vulnerability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".