Bibliographic record
Abstract
Intake fraction (iF), the proportion of emissions inhaled by an exposed population, is useful for prioritizing sources with the greatest impact on population exposure per unit emissions. This article reports iF estimates for urban winter wood smoke emissions. We used two approaches, incorporating spatiotemporal statistical models for (1) winter wood smoke fine particulate matter (PM2.5) emissions and concentration and (2) concentrations of levoglucosan (a wood smoke particulate marker). Empirical data used in our models were measured in Vancouver, Canada during 2004-2005. We used Monte Carlo simulations to quantify uncertainty. The estimated geometric mean iF (units: per million) is 13 (one geometric standard deviation range: 6.6-24) for wood smoke PM2.5 and 15 (4.5-50) for levoglucosan. These iF estimates are comparable to or slightly larger than iF values for urban vehicle emissions reported in the literature. On average, higher-income areas have lower wood smoke PM2.5 concentrations and intake. Our results emphasize the importance of urban wood smoke as a source of PM2.5 exposure and highlight the comparatively large population exposure and potential environmental justice benefits from reducing wood smoke emissions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".