The effects of urban density on the provision of multiple health-related ecosystem services
Bibliographic record
Abstract
Abstract Cities globally are expanding at an unprecedented rate, requiring an understanding of how to grow cities in a way that minimizes environmental impact while providing ecological benefits to people. Compact cities are often advocated for due to reduced impacts on biodiversity. However, increased development within an existing urban footprint may lead to loss of ecosystem services (ES) if accompanied by a loss of green space. We use spatial data and remote sensing approaches to explore relationships between urban form and indicators of health-related ES (temperature regulation, air pollution regulation, green space accessibility) at 250 study sites across a range of percent building cover in Montreal, Canada. We ask: 1) How does building cover and associated landscape structure affect multiple biophysical indicators linked to health-based ES? 2) Is population density related to ES provision at the scale of investigation once building cover is accounted for? Relationships between building cover and ES provision varied across the studied indicators. Loss of greenspace accompanying increased building cover did not affect air quality, for example, which depended strongly on pollutant sources. However, increased building cover – and accompanying vegetation loss – was a strong driver of higher daytime temperatures. For ES provided by greenspace access, there was a trade-off between the ability to provide public vs. private greenspace; suggesting public greenspace should be prioritized to maximize ES provision. Overall, our findings support that urban densification must be pursued with consideration for the overall landscape structure, and prioritize maintenance of vegetation in particular.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".