Forest-Fire Fine Particulate Matter and Daily Mortality in Greater Boston
Bibliographic record
Abstract
Forest-Fire Fine Particulate Matter and Daily Mortality in Greater BostonAbstract Number:1646 Ke Zu*, Christopher Long, Julie Goodman, and Peter Valberg Ke Zu* Gradient, United States, E-mail Address: [email protected] Search for more papers by this author , Christopher Long Gradient, United States, E-mail Address: [email protected] Search for more papers by this author , Julie Goodman Gradient, United States, E-mail Address: [email protected] Search for more papers by this author , and Peter Valberg Gradient, United States, E-mail Address: [email protected] Search for more papers by this author AbstractDuring July 2002, forest fires in Quebec, Canada, blanketed the US east coast with a plume of wood smoke. This ‘natural experiment’ exposed large populations in northeastern cities to significantly elevated concentrations of fine particulate matter (PM2.5), providing a unique opportunity to test the association between daily mortality and PM excursions uncorrelated with societal activity rhythms. We obtained PM2.5 24-hr concentrations and daily mortality counts for a four-week period in July 2002 for the Greater Boston Metropolitan area (population of over 4 million). Daily average concentrations of PM2.5 were markedly increased for three days over this period, reaching as high as 63 µg/m3 from background ambient levels of 5-30 µg/m3 in the non-smoke days. We examined temporal patterns of natural-cause deaths by a moving three-day window in the week following the smoke plume. Mortality rates were generally lower after the smoke event, compared to the average mortality for the rest of July 2002. Comparison to mortality rates over the same time period in 2001 and 2003 likewise showed no impact. In contrast, based on city-specific effect estimates for PM from previous studies, an expected increase of 2-9% in daily mortality (lag 1) was anticipated following this acute elevation in PM2.5 concentrations. In conclusion, substantial short-term increases in PM2.5 concentrations from forest fire smoke did not increase daily mortality in Greater Boston.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".