Development and characterization of an automated system for generation and collection of cigarette smoke (ASGCS) for rapid and accurate exposure and toxicity assessment
Bibliographic record
Abstract
Whole cigarette smoke condensate (WCSC) is a crucial tool for assessing tobacco product toxicity, offering advantages in storage, transportation, and concentration control. However, the lack of an automated system for WCSC production poses challenges in ensuring batch-to-batch consistency and test article reproducibility, both of which are essential for reliable toxicity evaluation. In this study, we developed and characterized an automated system for the generation and collection of cigarette smoke (ASGCS) to efficiently produce WCSC. Under Health Canada Intense smoking conditions, the ASGCS achieved a low smoke leakage rate of 9.45 ± 0.47 %, facilitating the collection of total particulate matter (TPM) at a relatively high concentration of 21.2 ± 0.2 mg cig –1 . Nicotine and total volatile organic compound concentrations in WCSC prepared using the ASGCS were 1.25 ± 0.01 μg cig –1 and 2276 μg cig –1 , corresponding to transfer rates of approximately 65 % and 135 % relative to cigarette smoke, respectively. WCSC exposure demonstrated a dose-dependent decrease in cell viability , with an IC 50 of 0.26 mg mL –1 (R 2 = 0.9171). These findings confirm the physical performance of ASGCS and the chemical and biological reliability of WCSC produced using this system. The ASGCS offers a robust platform for the rapid and accurate assessment of tobacco product toxicity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".