The Influence of Cigarette Designs and Smoking Regimes on Vapour Phase Yields
Bibliographic record
Abstract
Abstract Cigarettes with similar design features but with either cellulose acetate or dual carbon filters were made at 1-mg and 13-mg “tar” levels, as determined under the ISO smoking procedure. Products were smoked under the ISO, Massachusetts and Canadian smoking regimes to provide per-cigarette and per-puff yields of twelve vapour phase (VP) smoke components. The yields generated at the lit end of the cigarette and the significant yield reductions caused by filter ventilation, selective (carbon) adsorption, tobacco rod ventilation and diffusion were estimated in a modelling approach. For a “1-mg tar” carbon-filtered product it was estimated that the VP generated at the lit end was reduced by 99.4% to a machine yield of 17 µg/cig under ISO smoking conditions. Under the Canadian regime with 100% vent blocking, the estimated total VP was lowered 20% by tobacco rod effects and 15% by carbon filter adsorption giving a machine yield of 3487 µg/cig. The carbon filter adsorbed less efficiently partly due to the artificially high smoke temperatures through the filter that would probably not be tolerated by human smokers. Under the Massachusetts regime with 50% vent blocking, conditions better associated with human smoking, the total VP was lowered 51% by filter ventilation, 22% by tobacco rod effects and 17% by carbon filter adsorption giving a machine yield of 659 µg/cig. Ventilation is used to achieve “tar”/nicotine/carbon monoxide yield ceilings at 10/1/10 mg based on the current ISO smoking method. If future regulations were to mandate further reductions in VP then this will only be selectively achieved by increasing filter or tobacco rod ventilation/porosity or by using selective adsorption. It is inevitable that manufacturers will need to add further ventilation into their product to comply with such regulations and this should be reflected in any smoking regime. Furthermore, regimes with 100% vent blocking, that do not produce data reflecting the significant reductions in VP yields, provided to the smoker by ventilation, are misleading and their results will not correlate with relevant biomarker data. When proposing a different smoking regime, it is essential to understand the generation and transfer of smoke within cigarettes and factors involved in the subsequent data interpretation as described in this work. For regulatory evaluation purposes, cigarette characterisation using a regime that removes ventilation, one of the main design tools, is more misleading than the current ISO regime or one with partial vent blocking.
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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 | 0.001 | 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 | 0.000 | 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".