Tracking metal presence in cannabis vaping products from source to inhalation
Bibliographic record
Abstract
Vaping cannabis liquids is a convenient method of cannabis consumption, and is considered to be a less harmful alternative to smoking cannabis. However, vaping cannabis carries its own health risks, with many uncertainties, especially concerning the presence of metal particles in vape liquids. The heterogeneous distribution of these particles within the liquid matrix poses analytical challenges in measurement reproducibility. In this study, total metals analysis was performed on five samples from six different legal Canadian cannabis vape liquid products. The results indicated that metals from the vaping device components, cobalt (Co), nickel (Ni), and zinc (Zn) significantly contributed to within-batch variability. In contrast, all analyzed metals showed significant variability among tested products. Single-particle ICP-MS detected metal particles of aluminum (Al), Co, chromium (Cr), copper (Cu), Ni, tin (Sn), and Zn in the vape liquids. To assess potential consumer exposure, cannabis cartridges were vaped using a vaping machine and the resulting aerosol was analyzed. Although the number of detected emitted particles was below the limit of quantitation, all samples produced aerosols containing metal particles of Co, Cr, Ni, lead (Pb), Sn, and Zn. Further analysis using SEM-EDS on the emptied cartridges showed cracking on the connector pin of an unused device, suggesting a potential source of contamination during use and storage. The elemental composition of the metal components in the cartridges matched the detected particles, providing strong evidence that cannabis vape liquids are contaminated by the metal components of vaping devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".