How do different cigarette design features influence the standard tar yields of popular cigarette brands sold in different countries?
Bibliographic record
Abstract
OBJECTIVES: To examine the associations among cigarette design features and tar yields of leading cigarette brands sold in the United States, Canada, Australia and the United Kingdom. METHODS: Government reports and numbers listed on packs were used to obtain data on International Organization for Standardization (ISO)/Federal Trade Commission (FTC) yields for the tar of 172 cigarette varieties sold in the United States, Canada, Australia and the United Kingdom. We used standardised methods to measure the following 11 cigarette design parameters: filter ventilation, cigarette pressure drop, filter pressure drop, tobacco rod length, filter length, cigarette diameter, tipping paper length, tobacco weight, filter weight, rod density and filter density. RESULTS: Filter ventilation was found to be the predominant design feature accounting for the variations between brands in ISO/FTC tar yields in each of the four countries. After accounting for filter ventilation, design parameters such as overwrap length, tobacco weight and rod density played comparatively minor roles in determining tar yields. CONCLUSIONS: Variation in ISO/FTC tar yields are predicted by a limited set of cigarette design features, especially filter ventilation, suggesting that governments should consider mandatory disclosure of cigarette design parameters as part of comprehensive tobacco product regulations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".