Bibliographic record
Abstract
In November 2003 approximately 200 researchers, stakeholders, and policymakers from more than 40 countries gathered to discuss the science and policy implications of air pollution and human health as part of the AIRNET/NERAM Strategies for Clean Air and Health initiative. The purpose of this paper is to review the more than 35 research posters presented at the conference, including exposure, toxicological, and epidemiological studies of air pollution. Collectively, these papers support previous evidence that both short- and long-term exposures to particulate air pollution have adverse population health impacts, including effects on children. Cellular studies also suggest that air pollution can cause mutagenic and oxidative effects, raising concerns about carcinogenicity and cellular regeneration. Studies of biomarkers, such as Clara-cell proteins and lymphocyte damage assessment, provide further evidence of air pollution effects at the cellular level. Other studies have focused on improvements to measurement and sources of air pollution. These studies suggest that particle mass rather than particle composition may be a more useful indicator of potential human health risk. It is well known that emissions from transportation sources are a major contributor to ambient air pollution in large urban centres. Epidemiologic researchers are able to reduce bias due to misclassification and improve exposure assessment models by allocating air pollution exposure according to distance from traffic sources or land-use patterns. The close association between traffic patterns and air pollution concentrations provides a potential basis for the development of transport policies and regulations with population health improvements as a primary objective. The results of the research presented here present opportunities and challenges for the development of policies for improvements to air quality and human health. However, there remains the challenge of how best to achieve these reductions.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".