Existing technologies to reduce specific toxicant emissions in cigarette smoke
Bibliographic record
Abstract
BACKGROUND: The World Health Organization Tobacco Product Regulation (TobReg) study group has proposed emissions level performance standards for nine toxicants (NNN, NNK, acetaldehyde, acrolein, 1,3-butadiene, CO, BaP, benzene and formaldehyde, all expressed as micrograms per milligram nicotine as measured under the Canadian intensive method) in cigarette smoke for parties to the FCTC in conjunction with regular monitoring of emissions of nine other toxicants of interest, nicotine and nicotine-free dry particulate matter (NFDPM, or "tar"). METHODS: We examined the published literature and publicly available tobacco industry documents to determine the extent to which existing available technologies can be applied to reduce the emissions of the specified toxicants in cigarette smoke. RESULTS: Agricultural practices (for example, fertilisers, curing), plant characteristics (for example, protein content, nicotine content), tobacco blending (for example, American blend vs Virginia blend) and cigarette design (for example, additives, filters, paper) issues all have roles in the generation and reduction of specific smoke toxicants. The tobacco industry has explored a number of technologies, including selective filtration, changes to curing practices and rod additives to reduce specific toxicants. CONCLUSIONS: Technologies exist to reduce the toxicants identified by TobReg. The extent to which the industry is able to simultaneously reduce toxicants, however, is unknown.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".