Tobacco-specific N-nitrosamines NNN and NNK levels in cigarette brands between 2000 and 2014
Bibliographic record
Abstract
The evolution of the levels of tobacco-specific N-nitrosamines (TSNA), N-nitrosonornicotine (NNN) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) in mainstream (MS) cigarette smoke is investigated based on smoke and tobacco chemistry data of cigarette brands sold by Philip Morris International (PMI) between 2000 and 2014. A total of 315 cigarette samples representing a wide range of product and design characteristics manufactured by PMI between 2008 and 2014 were analyzed and compared to a previously published dataset of PMI brands manufactured in 2000. The data indicate that there is a substantial reduction of NNN and NNK levels in tobacco fillers and MS cigarette smoke per mg of tar and per mg of nicotine using Health Canada Intense (HCI) machine-smoking regime. This observed reduction in NNN and NNK levels in MS cigarette smoke is also supported by the downward trend observed on NNN and NNK levels in USA flue-cured Virginia and Burley tobacco lots from 2000 to 2014 crops, reflecting effectiveness of measures taken on curing and agricultural practices designed to minimize TSNA formation in tobacco.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".