The Impacts of Green Space Structure on Asthma in Toronto, Canada
Bibliographic record
Abstract
A series of studies have proved that green space can influence air pollution, which is the main risk factor for asthma. In addition, the biodiversity hypothesis suggests use of green space can contribute to increasing human commensal microbiota on the skin, which makes it possible to enhance immunity to allergic atopy. Given that, the co-effects of green space might jointly influence asthma. However, existing studies mainly focus on one or part of the pathway between them, and most of them shed light on the influence of greenness or trees on asthma. Green space structure, capable to influence both air pollution and microbial diversity, is also probably a significant factor to influence asthma. Regarding this, this study takes Toronto as the case to explore two potential mechanisms that shape the impact of green space structure on asthma. Additionally, tree diversity that might moderate this impact was also examined in this study. By regression analysis, it is found that the impact of green space structure on the prevalence of asthma by reducing air pollution was not evident. Moreover, green space structure has no significant contribution to the prevalence of asthma, but when moderated by tree diversity, it has the potential to reduce the prevalence of male asthmatics, and such moderation effect only works on male asthmatics. Except for that, several covariates selected in this study were also found to correlate with the prevalence of asthma. In particular, the relationship between household income and the prevalence of asthma is only significant among female asthmatics.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".