Traffic-related air pollution and allergic disease: an update in the context of global urbanization
Bibliographic record
Abstract
PURPOSE OF REVIEW: The review aims to give an update on the literature around traffic-related air pollution (TRAP) and allergic disease in the context of global urbanization, as the most populous countries in the world face severe TRAP exposure challenges. RECENT FINDINGS: As research continues to show that gene-environment interactions and epigenetics contribute to the TRAP-allergy link, evidence around the links to climate change grows. Greenspace may provide a buffer to adverse effects of traffic on health, overall, but pose risks in terms of allergic disease. SUMMARY: The link between traffic-related pollution and allergy continues to strengthen, in terms of supportive observational findings and mechanistic studies. Levels of TRAP across the world, particularly in Asia, continue to dramatically exceed acceptable levels, suggesting that the related adverse health consequences will accelerate. This could be counterbalanced by primary emission control and urban planning. Attention to combined effects of TRAP and allergen exposure is critical to avoiding misleading inferences drawn though examination only of isolated factors.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".