Climate change vulnerability assessment of the urban forest in three Canadian cities
Bibliographic record
Abstract
Climate change is a likely addition to the unpredictable challenges urban communities will face. Enhancing urban forests has gained prominence as a climate adaptation tool in cities. The fact that urban forests are also vulnerable is now starting to emerge. Many urban forest management professionals do not know how to take climate change into account and what aspects of urban forest vulnerability to climate change to prioritize.<br>Bringing climate change to the forefront of the decision-making process in urban forest management, and urban forests to the forefront of urban climate issues, is important to urban forest success. This paper presents an exploratory assessment of vulnerability to climate change in the Canadian urban forests of Halifax, London, and Saskatoon.<br>The objectives of the assessment were to:<br>1) identify the elements of urban forest exposure and sensitivity to climate change, the nature of the expected impact, and the adaptive capacities that exist in these three urban forests.<br>2) assess which of these elements contributes more to urban forest vulnerability to climate change.<br>3) elicit adaptive strategies based on this information. The method used was participatory and expert-based and allowed for a systematic evaluation of vulnerability. Exposures related to drought, heat stress, and wind, susceptibility of urban trees to insects and diseases, and the sensitivity of young trees and tree species with specific temperature and moisture requirements, are the main concerns regarding the vulnerability of urban forests to climate change in these three cities.
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 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 | 0.001 | 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) | 0.001 | 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 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".