Comparison of particulate matter emission and soluble matter collected from combustion cigarettes and heated tobacco products using a setup designed to simulate puffing regimes
Bibliographic record
Abstract
An experimental setup was designed and optimized to collect particulate matter suspended in cigarettes smoke or in the aerosol generated during the use of heated tobacco products (HTPs). The setup is mainly based on a gas washing flask containing water where particulate matter and soluble compounds can be trapped, resembling interaction in the body. This system allows to perform puffing experiments in conditions similar to those of the Health Canada Intense puffing regime. In the study, cigarettes and Heets (in IQOS heater) from Philip Morris Products were used. Complementary characterization of this particulate matter has been extracted from experiments on a gas washing flask containing isopropanol . Total organic carbon analysis, laser diffraction, UV-vis spectrophotometry and transmission electron microscopy measurements have shown that cigarette smoke contains a large number and content of compounds soluble in water and isopropanol, together with solid particles generated during combustion. In contrast, negligible presence of non-combustion related particles was detected in Heets aerosol samples. These facts, together with the higher amount of water soluble compounds and higher number of polyaromatic hydrocarbons detected in the cigarettes smoke, allow to conclude that HTPs are less harmful than combusted cigarettes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".