Classification of Cannabis Cultivars Marketed in Canada for Medical Purposes and Growth Trends of the North American Medical Cannabis Industry
Bibliographic record
Abstract
The recent legalization of cannabis for medical purposes in North America requires rigorous standardization of its phytochemical composition in the interest of consumer safety and medicinal efficacy. To utilize medicinal cannabis as a predictable medicine, it is crucial to classify hundreds of cultivars with respect to dozens of therapeutic cannabinoids and terpenes, as opposed to the current industrial or forensic classifications that only consider the primary cannabinoids tetrahydrocannabinol (THC) and cannabidiol (CBD). Labs-Mart recently developed and validated analytical methods using high-pressure liquid chromatography (HPLC-DAD) to quantify cannabinoids and gas chromatography with mass spectroscopy (GC-MS) to quantify terpenes in cannabis raw material. The methods were then used to classify 32 cannabis samples from two licensed producers into four clusters based on the content of 10 cannabinoids and 14 terpenes. The classification results were confirmed by cluster analysis and principal component analysis in tandem, which were distinct from those using only THC and CBD. Three potential trends are postulated for the medical cannabis industry in North America, the success of which may rely on a systematic classification of cannabis cultivars and the establishment of reliable testing methods on cannabis raw materials and cannabis extract products. These unprecedented growth areas include the increasing prevalence of cannabis oil and edible products, the potential of synergism with other analgesics, and the utilization of each part of cannabis plant.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".