Selected Constituent Yield Variation in the Smoke of Commercial Cigarette Brands on the Japanese Market
Bibliographic record
Abstract
Summary This study focused on the variation in the yields of constituents in smoke from commercial cigarette brands available on the Japanese market. Nineteen commercial cigarette brands were sampled five times every two months from 2009 to 2010. The target constituents were benzo[ a ]-pyrene, 1,3-butadiene, benzene, formaldehyde, acetaldehyde, acrolein, N -nitrosonornicotine (NNN), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), carbon monoxide, “tar”, and nicotine. The results of this study showed that the coefficient of variation (CV) values varied greatly by brands, constituents, and smoking regimes. The yields of NNN and NNK in the smoke were strongly correlated to their yields in the tobacco filler blend for most brands. The yields of benzo[ a ]pyrene under the International Organization for Standardization (ISO) and the Health Canada Intense (HCI) smoking regimes and 1,3-butadiene under the HCI smoking regime were found to be influenced by the measurement. It was shown that factors for variation were highly varied among constituents. The grand mean of CV values for NNN and formaldehyde associated with cigarette manufacturing over ten months and measurement at the JT laboratory under the HCI smoking regimes were 17.1% and 6.6% respectively. The grand mean of CV values for NNN and formaldehyde associated with both cigarette manufacturing over ten months and measurement at different laboratories under the HCI smoking regimes were 23.7% and 22.9% respectively. This is due to the fact that formaldehyde showed the highest CV values for reproducibility among the constituents. Thus, in order to set realistic and robust confidence intervals, it is very important to take into account the variations associated with cigarette manufacturing and measurement within and between laboratories.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".