Trees and Life, Heat and Death: Integrating Temperature and Green Spaces with Social Determinants of Health in Hamilton, Ontario, Canada.
Bibliographic record
Abstract
Objectives Climate change has wide-reaching implications for planetary and human health; one of its rising impacts is deaths related to extreme heat. This study attempts to integrate remotely sensed measures of temperature and greenness into the methodology of Code Red, a study examining the relationship between health and a variety of social determinants in Hamilton, Ontario, Canada, initially using data from 2006-2008, with the aim of examining whether heat and temperature could explain differences in the average age of death across different neighbourhoods alongside socioeconomic variables. Methods Land surface temperature (LST) and normalized difference vegetation index (NDVI) were calculated for each census tract of Hamilton using Landsat satellite data for Jun-Aug of 2006-2008. They were then entered into a factor analysis along with 14 other variables utilized in the initial Code Red study. A multiple least-square regression was then run between the resulting factors and average age of death. Results Temperature and greenness loaded significantly along with income and education related variables onto a factor referred to as the “working class” factor. This factor had a highly significant (p<0.001) correlation with average age of death in multiple regression. Conclusion Temperature and greenness have a significant correlation with socioeconomic deprivation and age of death, and may have value both as a predictor variable for death and a potential cause of increased deaths. Further studies may make use of detailed cause of death data or change-over-time analysis.
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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 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".