Shade Trees in Schoolyards: their Role in Protection from Ultraviolet Rradiation and Improving Childhood Learning
Bibliographic record
Abstract
Urban trees are widely recognized to improve the physical environment for humans, but little work has been done to quantify differences among tree species in their ability to mitigate ultraviolet radiation, or on tree effects on learning in the context of primary education. My research examines the environmental services of schoolyard shade trees, and specifically evaluates the potential for different tree species to attenuate UV radiation reaching the ground and to provide psychological benefits as indicated by standardized test score performance. My thesis examines three different approaches to address specific questions that add to our understanding of both mechanisms and net effects, namely: (1) the analysis of leaf optical and morphological traits and their impact on the transmittance and reflectance of UV radiation, particularly UV-B and UV-C radiation of most concern from a human health perspective; (2) the determination of typical UV radiation levels beneath different urban tree species in schoolyards and public parks across Toronto, Ontario; and (3) the assessment of tree canopy cover, diversity, and species composition on the academic performance of primary-school-age children. I found both estimated leaf-level UV reflectance and transmittance were substantially higher than prior published estimates. UV reflectance ranged from 20.06-49.69% (UV-A), 30.13-62.68% (UV-B), and 37.18-73.29% (UV-C), and UV transmittance ranged from 0.33-10.14% (UV-A), 0.44-13.13% (UV-B), and 1.61-29.73% (UV-C) among sampled leaves. When analyzed at the canopy-level, UV protection factor (PF) varied significantly among species, ranging from ~1.3-3.4. Additionally, I found crown transparency (%), crown radius/height of live crown (m), and shade tolerance were important as predictors of UV protection factors. Lastly, tree cover is a significant positive predictor of children’s academic performance, and the effects of tree cover and species composition were most apparent in schools that showed the highest level of external challenges to excel in academic settings. More broadly, this dissertation examines the potential for shade trees in typical urban settings to provide both physical and mental health benefits to children through the attenuation of UV radiation and enhanced educational performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".