Mouth Level Exposure and Similarity to Machine-smoked Constituent Yields
Bibliographic record
Abstract
OBJECTIVES: The goal of this review was to evaluate which standard machine-smoking regimen may be most appropriate to inform tobacco product regulation based on the fraction of cigarette smoke yields that best represents the range of human smoke exposures. METHODS: We searched PubMed and Web of Science to identify peer-reviewed studies that reported percentages of smokers who smoked more or less like a particular machine-smoking regimen based on human mouth level exposure (MLE) tobacco constituent yields. RESULTS: Three studies met our inclusion criteria and were reviewed. Studies found that Canadian Intense (CI) yields were equal to or greater than 86% to 97% of smokers' nicotine and tar MLE yields. CONCLUSIONS: MLE yields indicate that a small percentage of individuals (less than 14%) are exposed to nicotine and tar yields equal to or greater than those measured by the CI regimen. Whereas no machine-smoking regimen reflects human puffing behavior with complete accuracy, based on MLE data, CI constituent yields constitute the best representation of exposure that encompasses the majority of smokers, and may be the most informative for regulatory purposes.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".