Carbonyls emissions during vaping of herbal tea sticks
Bibliographic record
Abstract
Herbal tea sticks can be vaped using heated tobacco products devices. Although they are very recently appear in the market, their market share is expected to grow in the future. This work focuses on the carbonyls’ emissions from four herbal tea sticks, of two different flavors, with and without nicotine (Unicco-flavor Lemon (L_Un), Ccobato-flavor Lemon (L_Cc), Unicco-flavor Peach (P_Un), Ccobato-flavor Peach (P_Cc)). Tea sticks are heated using IQOS and LIL heated tobacco products (HTPs) devices. The emissions are generated using a peristaltic pump, under ISO and Health Canada Intense puffing regimes. The carbonyls are collected in an acidified DNPH solution and analyzed by HPLC-UV. Acetaldehyde, Propionaldehyde and Butyraldehyde were found above the detection limit, while the emissions of Formaldehyde and of the other carbonyls were below the detection limit. The results show that the dominant carbonyl is acetaldehyde, followed by butyraldehyde and propionaldehyde. The HCI/CAN puffing regime leads, in general, to an increase of emissions. The presence of nicotine in the tea stick, the stick flavor and the HTP device used for heating did not significantly affect the emissions. erj;66/suppl_69/PA3768/F1 F1 F1 erj;66/suppl_69/PA3768/F2 F2 F2
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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) | 0.003 | 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".