Investigation of number of replicate measurements required to meet cigarette smoke chemistry regulatory requirements measured under Canadian intense smoking conditions
Bibliographic record
Abstract
There has been increased interest in recent years in regulatory reporting of cigarette smoke toxicants. There is a great deal of diversity in current regulatory standards around the world in terms of the identities of regulated toxicants, and the number of replicate analyses stipulated for their measurement. Furthermore, analytical methods developed collaboratively by several organisations and intended for regulatory analysis generally differ in their recommended replicate numbers to those stipulated by regulators. In view of these inconsistencies, we undertook an exercise to examine the most appropriate numbers of replicates required for regulatory analysis of cigarette smoke toxicants. A one-point-in-time sampling exercise was undertaken of the German cigarette market, with 161 brands sampled and analysed in a single laboratory using Canadian Intense smoking conditions. Seven replicate measurements were made for each analyte and product, other than nicotine, CO and nicotine-free dry particulate matter for which eight replicate measurements were made. After confirming the absence of order of analysis effects, a variety of statistical tests (such as group assessment, paired comparisons, linear regression models and ratio analysis) were conducted examining mean values, SDs and CVs to identify the role of numbers of analytical replicates on data quality. The statistical analysis showed no difference in mean values for any of the 18 toxicants irrespective of replicate numbers (between 3 and 7 or 8). The large majority of analytes showed no difference in data variability with replicate number; but some very small differences (much lower than within product variability) were observed for a minority of compounds. Similarly, paired analysis showed no significant differences between mean values obtained using different replicate numbers in most cases, apart from very low differences (<5%) for a small number. Linear regression analysis showed correlations around 96 to 98% (other than crotonaldehyde at 91%) between values obtained with 3 vs 7 replicates. Similarly, per product mean value ratio analysis showed 95% consistency between values obtained with 3 and 7 replicates. We therefore conclude that three replicates is sufficient for precise determination of cigarette mainstream smoke toxicant emissions, and that use of 7 replicates as stipulated in some regulator jurisdictions does not offer any greater accuracy or precision.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".