Measurement Science for Enhanced Cannabis Testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it