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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".