Measuring public health accountability of air quality management
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
Accountability of air quality management is often measured by tracking ambient pollution concentrations over time. These changes in ambient air quality are rarely linked to changes in public health, a major driver for such programs. We propose a method to assess the accountability of air quality management programs with respect to improvements in public health by estimating national temporal trends in health risk attributable to air pollution. The air health indicator (AHI) is a function of two temporal functions, annual air pollutant concentrations and annual estimates of health risk obtained by time series statistical methods, to indicate the trend in annual percent attributable risk (the product of concentration and risk times 100). Random effects models are used to obtain a distribution of risk over space. The model is illustrated by examining the association between daily nonaccidental deaths in 24 of Canada’s largest cities and daily concentrations of ozone and nitrogen dioxide over the 17-year period 1984–2000. Our analysis demonstrates that examining trends in exposure alone, which has typically been the approach to air quality indicators, provides an incomplete picture of trends in the impact of air pollution. The AHI appears to provide a more informative measure of the population burden of illness associated with air pollution over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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