Dependence of tar, nicotine and carbon monoxide yields on physical parameters: implications for exposure, emissions control and monitoring
Bibliographic record
Abstract
OBJECTIVE: To estimate the extent to which tar, nicotine and carbon monoxide (TNCO) yields are dependent on cigarette design features such as burn rate, filter ventilation and paper porosity, and to consider the implications for human exposure and the regulation of TNCO emissions. A related aim is to determine whether accurate prediction of TNCO yields is possible using only simple physical parameters. DESIGN AND METHODS: Datasets that include quantitative design parameters as well as measurements of TNCO yields collected under standard conditions with vents unblocked (International Organization for Standardization) and under intense conditions with vents fully blocked (Health Canada) were compiled from the literature (primarily US and UK brands). Forward stepwise multiple regression analysis is used to assess the relative importance of each design feature in explaining variability in the observed emissions. Using randomly split data subsets, multiple linear regression is used to model the dependence of TNCO yields on design features in the training subset and validated against the test subset. Tar and carbon monoxide correlate with many of the particulate- and volatile-phase toxins in smoke, and brand values normalised to nicotine yield are used as surrogate measures of exposure within the bounds defined by non-intense and intense smoking protocols. RESULTS AND CONCLUSIONS: Filter ventilation is the dominant control on measured TNCO emissions, but other factors including burn rate, amount of tobacco and paper porosity also contribute. Yields are predictable with reasonable accuracy and precision using only measured physical parameters. Surrogate exposure indicators suggest that filter ventilation does not lead to any reduction in exposure and that highly ventilated (low-yield) brands may actually increase exposure to the more volatile toxins.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".