Bibliographic record
Abstract
Exposure to fine airborne particles (PM2.5) increases the risk of health problems like heart attacks. Limits on this pollution in many countries account for only the particles’ size, not their composition. Now research by Scott Weichenthal, an environmental epidemiologist at McGill University, suggests that chemistry matters. People who are exposed to PM2.5 with higher levels of sulfur and transition metals are more likely than people exposed to PM2.5 without that chemistry to go to the hospital for an acute cardiovascular event. Weichenthal’s team used hospital admission data, daily ambient PM 2.5 measurements, and monthly chemical analysis of particles captured at 34 locations in Canada. People living near sites where PM 2.5 was elevated in both sulfur and transition metals were at higher risk of a cardiovascular event ( Environ. Health Perspect. 2021, DOI: 10.1289/EHP9449 ). High levels of S and copper, for example, increased the risk by about 10%, Weichenthal
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".