Using a probabilistic model (pCNEM) to estimate personal exposure to air pollution
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
Abstract This article describes the use of a probabilistic model to estimate personal exposure to airborne pollutants. Such estimates are important when assessing, for example, the potential effects of air pollution on health and in developing related policy. An individual's personal exposure will be determined by local pollution sources which will change throughout the day as the individual's location changes. For this reason, models have been developed that utilize ‘time activity’ patterns to compute the overall exposure to pollutants. The model described here is referred to as ‘pCNEM’ and can be accessed through the WWW. The computational platform is flexible in that it allows users to construct models defining local sources of pollution and emissions in addition to ambient levels. This article demonstrates the construction of such a model, for predicting the exposure to PM 10 of random selected individuals from sub‐populations of Greater London. A case study of working females in the spring and summer of 1997 is presented. Copyright © 2005 John Wiley & Sons, Ltd.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it