Multiresidue Method of Analysis of Pesticides in Medical Cannabis
Bibliographic record
Abstract
Three related analytical methods were developed and validated for the determination of pesticides in cannabis leaves, dried cannabis flowers, and cannabis oil. The methods follow the generic sequence of an acetonitrile extraction, followed by solid-phase extraction cleanup and analysis by HPLC-tandem mass spectrometry (HPLC-MS/MS), GC-MS/MS, and GC-MS. These methods were developed to accommodate sample quantity and lipid content of the different matrices. Validation at a spiking level of 0.01 μg/g was successful for 39 pesticides in cannabis leaves and 40 pesticides in cannabis oil, and at 0.02 μg/g for 32 pesticides in cannabis flowers, with the majority of analytes showing recoveries within the acceptable range of 70-130%. With these methods established, unannounced inspections of Canadian licensed producers of cannabis revealed that out of 144 samples collected, 26 showed the presence of unauthorized pest control products.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".