Strategies for Clean Air and Health∗
Bibliographic record
Abstract
An international colloquium, "Strategies for Clean Air and Health," was organized by the Network of Environmental Risk Assessment and Management (NERAM) and the AIRNET European Network on Air Pollution and Health to identify directions for air quality policy development and research priorities to improve public health. A conference statement was prepared to provide guidance from the perspective of an international group of scientists, regulators, industries, and interest groups on a path forward to improve the interface between science and clean air policy strategies to protect public health. The statement represents the main findings of two breakout group discussion sessions, supported by perspectives of keynote speakers from North America and Europe on science-policy integration and views of the delegates expressed in plenary discussions. NERAM undertook a carefully considered process to try to ensure that the statement would accurately reflect the conference discussions, including documentation of supporting comments from the proceedings and inviting delegates' comments on two draft versions of the statement.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".