Conifers May Ameliorate Urban Heat Waves Better Than Broadleaf Trees: Evidence from Vancouver, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Anthropogenic greenhouse gas emissions are increasing the frequency of deadly heat waves. Heat waves are particularly devastating in cities, where air pollution is high and air temperatures are already inflated by the heat island effect. Determining how cities can ameliorate extreme summer temperature is thus critical to climate adaptation. Tree planting has been proposed to ameliorate urban temperatures, but its effectiveness, particularly of coniferous trees in temperate climates, has not been established. Here, we use remote sensing data (Landsat 8), high-resolution land cover data, and Bayesian models to understand how different tree and land cover classes affect summer surface temperature in Metro Vancouver, Canada. Although areas dominated by coniferous trees exhibited the lowest albedo (95% CrI 0.08–0.08), they were significantly (12.2 °C) cooler than areas dominated by buildings. Indeed, we found that for conifers, lower albedo was associated with lower surface temperatures. Planting and maintaining coniferous trees in cities may not only sequester CO2 to mitigate global climate change, but may also ameliorate higher temperatures and deadly heat waves locally.
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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 | 0.000 | 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) | 0.020 | 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 it