Quantitative determination and validation of 96 pesticides in cannabis by LC-MS/MS and GC-MS/MS
Bibliographic record
Abstract
Canada has established a strict set of testing requirements for pesticides that are unauthorized for use on cannabis, currently holding the most extensive list of analytes in North America to date, listing minimum method performance limits rather than maximum allowable concentration limits. These requirements establish the need for validated analytical methods capable of quantifying pesticides and growth regulators in highly variable cannabis inflorescence prior to distribution and sale. We have developed quantitative LC-MS/MS and GC-MS/MS methods capable of quantifying the 96 pesticides unauthorized in Canada for use on cannabis in dried cannabis flower and hemp. Herein, we report the validation results for linearity, precision, within- and between-sample accuracy, recovery, ion suppression, and limits of quantitation in dried cannabis inflorescence. Accuracy is evaluated in 10 cultivars varying in major cannabinoid content as well as ground whole-plant hemp to demonstrate method performance over a range of sample types. Results of the application of the current method to six cannabis samples seized from illegal storefronts by the Ontario Provincial Police are also reported.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".