A conceptual framework of urban forest ecosystem vulnerability
Bibliographic record
Abstract
The urban environment is becoming the most common setting in which people worldwide will spend their lives. Urban forests, and the ecosystem services they provide, are becoming a priority for municipalities. Quantifying and communicating the vulnerability of this resource are essential for maintaining a consistent and equitable supply of these ecosystem services. We propose a theory-based conceptual framework for the assessment of urban forest vulnerability that integrates the biophysical, built, and human components of urban forest ecosystems. A review and description of potential vulnerability indicators are provided. Urban forest vulnerability can be defined as the likelihood of decline in ecosystem service supply and its associated benefits for human populations, urban infrastructure, and biodiversity. It is comprised of (i) exposure, which refers to the stressors and disturbances associated with the urban environment that negatively affect ecosystem function, (ii) sensitivity, which is determined by urban forest structure and dictates the system response to forcing from exposures and the magnitude of potential impacts, and (iii) adaptive capacity, which is the social and environmental capacity of a system to shift or alter its conditions to reduce its vulnerability or to improve its ability to function while stressed. Potential impacts, or losses in ecosystem service supply, are temporal in nature and require backward-looking monitoring and (or) forward-looking modelling to be measured and assessed. Vulnerability can be communicated through the use of indicators, aggregated indices, and mapping. A vulnerability approach can communicate complex issues to decision-makers and advance the theoretical understanding of urban forest ecosystems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".