Herb vaping produces carbonyls in mainstream smoke
Bibliographic record
Abstract
In recent years there has been a shift towards herb vaping; however, there are very few studies determining the compounds emitted. This work studies the carbonyls emitted in the mainstream emissions of a herb vaporizer using: chamomile(CHAM), cannabis(CANN), thyme(THYM), lavender(LAV), passiflora(PASS), green tea(GREEN), eucalyptus(EUC), at 215 and 225°C, under International Organization for Standardization (ISO) and Health Canada Intense (HCI/CAN) puffing regimes. The carbonyls are collected in acidified DNPH solution and analyzed by HPLC-UV. Five carbonyls were detected. Formaldehyde, propionaldehyde and crotonaldehyde were below the limit of detection. Acetaldehyde and butyraldehyde were found to be predominant in the carbonyl content of herbs. The first is found at 2-5 times higher concentrations than the latter. The results (Fig 1, Fig 2) show that emission of carbonyls increase with temperature. The impact of smoking regime is not clear. There is a high variability on the emission of carbonyls as a function of herb. CANN emits more acetaldehyde than the other herbs, while CHAM is the herb that emits the most butyraldehyde. Fig 1. Emission of acetaldehyde <fig><object-id>erj;64/suppl_68/PA4030/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig> Fig 2. Emission of butyraldehyde <fig><object-id>erj;64/suppl_68/PA4030/F2</object-id><object-id>F2</object-id><object-id>F2</object-id><graphic></graphic></fig>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".