Addressing Global Mortality from Ambient PM<sub>2.5</sub>
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
Ambient fine particulate matter (PM2.5) has a large and well-documented global burden of disease. Our analysis uses high-resolution (10 km, global-coverage) concentration data and cause-specific integrated exposure-response (IER) functions developed for the Global Burden of Disease 2010 to assess how regional and global improvements in ambient air quality could reduce attributable mortality from PM2.5. Overall, an aggressive global program of PM2.5 mitigation in line with WHO interim guidelines could avoid 750 000 (23%) of the 3.2 million deaths per year currently (ca. 2010) attributable to ambient PM2.5. Modest improvements in PM2.5 in relatively clean regions (North America, Europe) would result in surprisingly large avoided mortality, owing to demographic factors and the nonlinear concentration-response relationship that describes the risk of particulate matter in relation to several important causes of death. In contrast, major improvements in air quality would be required to substantially reduce mortality from PM2.5 in more polluted regions, such as China and India. Moreover, forecasted demographic and epidemiological transitions in India and China imply that to keep PM2.5-attributable mortality rates (deaths per 100 000 people per year) constant, average PM2.5 levels would need to decline by ∼20-30% over the next 15 years merely to offset increases in PM2.5-attributable mortality from aging populations. An effective program to deliver clean air to the world's most polluted regions could avoid several hundred thousand premature deaths each year.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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