The influencing factors of health hazards of benzo[a]pyrenein cigarette mainstream smoke: The example of one brand inBeijing
Bibliographic record
Abstract
INTRODUCTION: The study focused on the influence of tar concentrations, smoking regimen, and smoking behavior, on benzo[a]pyrene (B[a]P) emission from cigarette mainstream smoke and related health hazards to determine the key factors influencing B[a]P reduction and protection of the smoker's health. METHODS: A locally popular brand of cigarettes in Beijing was selected with tar concentrations of 1, 3, 5, 8, 10, and 11 mg/cigarette. Two different machine smoking regimens, the Canada Intense (HCI) regimen and the International Organization for Standardization (ISO) regimen, were adopted to collect the cigarette mainstream smoke. The B[a]P emission concentrations were then measured by gas chromatography and mass spectrum. RESULTS: The average B[a]P emission was 8.14-17.6 ng/cigarette for the HCI regimen and 0.92-3.46 ng/cigarette for the ISO regimen. As expected, the tar concentrations and B[a]P emissions exhibited a positive relation in both the HCI and ISO regimens, the cancer risk and non-cancer risk increased with an increase in tar concentrations for both the ISO and HCI regimens, and the smoking behavior also affected the B[a]P emissions with a tendency of VB (ventilation blocking) > HVB (half ventilation blocking) > DP (deep puff), under the same smoking regimen. Under the same conditions, the cancer risk and non-cancer risk in men were 1.19 and 1.11 times, respectively, higher than in women. CONCLUSIONS: The smoking regimen influences the B[a]P emission relatively more than the cigarette tar concentration and smoking behavior. The cancer risk and non-cancer risk are higher in men than in women that possibly due to longer smoking duration and greater smoking intensity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".