Empirical characterisation of ranges of mainstream smoke toxicant yields from contemporary cigarette products using quantile regression methodology
Bibliographic record
Abstract
Approximately 100 toxicants have been identified in cigarette smoke, to which exposure has been linked to a range of serious diseases in smokers. Smoking machines have been used to quantify toxicant emissions from cigarettes for regulatory reporting. The World Health Organization Study Group on Tobacco Product Regulation has proposed a regulatory scenario to identify median values for toxicants found in commercially available products, which could be used to set mandated limits on smoke emissions. We present an alternative approach, which used quantile regression to estimate reference percentiles to help contextualise the toxicant yields of commercially available products with respect to a reference analyte, such as tar or nicotine. To illustrate this approach we examined four toxicants (acetone, N'-nitrosoanatabine, phenol and pyridine) with respect to tar, and explored International Organization for Standardization (ISO) and Health Canada Intense (HCI) regimes. We compared this approach with other methods for assessing toxicants in cigarette smoke, such as ratios to nicotine or tar, and linear regression. We concluded that the quantile regression approach effectively represented data distributions across toxicants for both ISO and HCI regimes. This method provides robust, transparent and intuitive percentile estimates in relation to any desired reference value within the data space.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".