Not just ‘a few wisps’: real-time measurement of tobacco smoke at entrances to office buildings
Bibliographic record
Abstract
INTRODUCTION: An unintended consequence of indoor smoking restrictions is the relocation of smoking to building entrances, where non-smokers may be exposed to secondhand smoke, and smoke from outdoor areas may drift through entrances, exposing people inside. Tobacco smoke has been linked to numerous health effects in non-smokers and there is no safe level of secondhand smoke (SHS) exposure. This paper presents data on levels of tobacco smoke inside and outside entrances to office buildings. METHODS: Real-time air quality monitors were used to simultaneously measure respirable particulate matter (PM(2.5); air pollutant particles with a diameter of 2.5 μg or less) as a marker for tobacco smoke, outside and inside 28 entrances to office buildings in downtown Toronto, Ontario, in May and June 2008. Measurements were taken when smoking was and was not present within 9 m of entrances. Background levels of PM(2.5) were also measured for each session. A mixed model analysis was used to estimate levels of PM(2.5), taking into account repeated measurement errors. RESULTS: Peak levels (10 s averages) of PM(2.5) were as high as 496 μg/m(3) when smoking was present. Mixed model analysis shows that the average outdoor PM(2.5) with smoking was significantly higher than the background level (p<0.0001), and significantly and positively associated with the number of lit cigarettes (p<0.0001). The average level of PM(2.5) with ≥ 5 lit cigarettes was 2.5 times greater than the average background level. CONCLUSIONS: These findings support smoke-free policies at entrances to buildings to protect non-smokers from exposure to tobacco smoke.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".