High levels of pesticides found in illicit cannabis inflorescence compared to licensed samples in Canadian study using expanded 327 pesticides multiresidue method
Bibliographic record
Abstract
BACKGROUND: As Cannabis was legalised in Canada for recreational use in 2018 with the implementation of the Cannabis Act, Regulations were put in place to ensure safety and consistency across the cannabis industry. This includes the requirement for licence holders to demonstrate that no unauthorized pesticides are used to treat cannabis or have contaminated it. In this study, we describe an expanded 327 multi-residue pesticide analysis in cannabis inflorescence to confirm if the implementation of the Cannabis Act is providing safer licensed products to Canadians in comparison to those of the illicit market. METHODS: An extensive multi-residue method was developed using a modified quick, easy, cheap, effective, rugged, and safe (QuEChERS) sample preparation method using a combination of gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS) and liquid chromatography-triple quadrupole mass spectrometry (LC-MS/MS) for the simultaneous quantification of 327 pesticide active ingredients in cannabis inflorescence. RESULTS: Application of this method to Canadian licensed inflorescence samples revealed a 6% sample positivity rate with only two pesticide residues detected, myclobutanil, and dichlobenil, at the method's lowest calibrated level (LCL) of 0.01 μg/g. Canadian illicit cannabis inflorescence samples analysed showed a striking contrast with a 92% sample positivity rate covering 23 unique pesticide active ingredients with 3.7 different pesticides identified on average per sample. Chlorpyrifos, imidacloprid, and myclobutanil were measured in illicit samples at concentrations up to three orders of magnitude above the method LCL of 0.01 μg/g. CONCLUSION: These results demonstrate the need of an extensive multiresidue method capable of analysing hundreds of pesticides simultaneously, to generate data for future policy and regulatory decision-making, and to enable Canadians to make safe cannabis choices.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".