Assessing the vulnerability of urban forests to climate change
Bibliographic record
Abstract
Climate adaptation is being embraced by many municipalities worldwide. An element of this is the planting and protection of urban trees. However, the fact that climate change will also have an impact on urban trees has been largely overlooked. We argue that climate vulnerability assessments are necessary for addressing climate adaptation in urban forests and contribute to successful climate adaptation in cities. We review and integrate the literature on climate vulnerability and urban forests to explore how the general notion of urban forest vulnerability to climate change can be developed into an operational framework for undertaking a vulnerability assessment. The framework characterizes climate exposure, impact, sensitivity, and adaptive capacity, as well as nonclimatic drivers and factors, in urban forests. The most important themes in this discussion include urban tree species selection and diversity, naturalization, resource access, social awareness and engagement, budget and economic valuation, liability issues, and governance structures. Climate change vulnerability assessments help us understand how and why urban forests are vulnerable to climate change, identify future areas for research, and determine what adaptation measures could be included in urban forest management. These assessments help bring climate change to the forefront of the decision-making process and contribute to successful urban adaptation to climate change.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".