Development of a Multi Pollutant Model to Assess Air Pollution Association with Human Health Effects
Why this work is in the frame
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Bibliographic record
Abstract
A number of methodologies have been developed for investigation of the association between human health and exposure to a single pollutant [e.g., 1]. However, as pollutants are correlated, and the joint effect of pollutants is of high interest, work continues on development for multiple pollutant models. In this work, we discuss a method using Thin Plate Splines (TPS) to simultaneously model both PM2.5 (particulate matter less than 2.5 m in aerodynamic diameter) and O3 (ozone) in association with human mortality. The results are compared to effect estimates obtained from single pollutant models. We find similar temporal trends in the estimates, with large movements in both PM2.5 and O3 being captured in the TPS estimates. The estimated errors for the TPS method are larger than the individual models combined and produce risks that are comparable but slightly elevated.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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