Land‐use history causes differences in park nighttime cooling capacity and forest structure
Bibliographic record
Abstract
Cities are temporally dynamic ecosystems that experience continuous redevelopment over time. Urban parks, which provide critical benefits to resident wellbeing, are developed on different land-use types and contain natural elements that are susceptible to the effects of historical decision-making. Thus, understanding the current day functioning of our city's ecosystems and planning for the equitable distribution of park benefits in the future requires incorporation and understanding of the impacts of historical decision-making. We measured neighborhood sociodemographic composition, forest structure, and the cooling effect of 33 sites in parks across Montreal, each with a past land-use in one of three classes: agricultural, forested, or industrial. We asked the following questions: (1) what are the effects of historical land-use on current park forest structure, diversity, and consequently the capacity to provide cooling? (2) how do surrounding communities differ around parks of each historical land-use type, and what are the implications for equitable access to cooling? We found that forest structure and cooling capacity differed across past land-use type, and forest structure has complex relationships with park cooling capacity. Our results provide evidence of historical environmental injustice impacting current day cooling capacity for marginalized groups. Previously industrial parks had less cooling capacity at night, while simultaneously being surrounded by communities with higher proportions of immigrants and lower median incomes than Montreal's average. However, daytime cooling capacity was similar across past land-use type, highlighting the importance of current management decisions to provide a critical ecosystem service, temperature mitigation, regardless of a site's history. Planting areas of small, dense forest stands with trees ≥5 cm dbh within urban parks can help augment daytime cooling benefits in the city but may hinder nighttime cooling. To provide both nighttime and daytime cooling, a mixed management strategy of park trees is required, where small and large trees are incorporated at different densities. Finally, we find evidence of gentrification surrounding all park sites, emphasizing the complex socioecological dynamics of green infrastructure and the need for community-led greening projects paired with social housing policies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".