Measurement Science for Enhanced Cannabis Testing
Bibliographic record
Abstract
Advancements in measurement science are vital in assuring the quality and safety of Canada's rapidly expanding cannabis industry. Reports of high variability in results between testing laboratories, in addition to several medical cannabis recalls, have highlighted the need for standards in cannabis testing. The National Research Council, as Canada's National Metrology Institute, is addressing this challenge through the promotion of documentary standards for cannabis testing methods and the development of cannabis certified reference materials (CRMs). These standards will ensure accuracy and consistency of testing results, assist licensed cannabis producers in achieving regulatory requirements, and ultimately promote confidence in the regulated cannabis industry. This presentation will highlight recent advancements in metrology in support of the cannabis industry. Specifically, a liquid chromatography – tandem mass spectrometry (LC-MS/MS) method for cannabinoids will be described, which is being proposed as a candidate ASTM International standard method. A pesticide method using liquid chromatography – high resolution mass spectrometry (LC-HRMS) will also be discussed. The presentation will also highlight progress on the development of a cannabis CRM (NRC MARI-1) to be certified for major cannabinoids and select contaminants. The production and certification will discussed, including the establishment of SI-traceability through the purity assignment of cannabinoid standards by quantitative nuclear magnetic resonance spectroscopy (qNMR). The availability of a cannabis CRM will facilitate method validation for cannabis testing laboratories and allow laboratories to assess their entire method, from extraction to analysis.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".