Summary of the workshop on methodologies for environmental public health tracking of air pollution effects
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
The US Centers for Disease Control and Prevention established the Environmental Public Health Tracking (EPHT) program to support state and local projects that characterize the impact of the environment on health. The projects involve compiling, linking, analyzing, and disseminating environmental and health surveillance information, thereby engaging stakeholders and guiding actions to improve public health. One of the EPHT objectives is to track the public health impact of ambient air pollution with analyses that are timely and relevant to state and local stakeholders. To address methodological issues relevant to this objective, in January 2008, government officials and researchers from the USA, Canada, and Europe gathered in Baltimore, Maryland for a 2-day workshop. Using commissioned papers and presentations on key methodological issues as well as examples of previous air pollution impact assessments, work group discussions produced a set of consensus recommendations for the EPHT program. These recommendations noted the need for data that will encourage local stakeholders to support continued progress in air pollution control. The limitations of using only local data for analyses were also noted. To improve local estimates of air pollution health impacts, methods were recommended that "borrow strength" from other evidence. An incremental approach to implementing such methods was recommended. The importance and difficulty of communicating uncertainties in local health impact assessments was emphasized, as was the need for coordination among different agencies conducting health impact assessments.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it