Cannabis chemovar classification: terpenes hyper-classes and targeted genetic markers for accurate discrimination of flavours and effects
Bibliographic record
Abstract
The classification of Cannabis varieties has been increasingly discussed in the past years, particularly in the wake of emerging legal markets, with implications for intellectual property development, marketing and improvement of the scientific understanding of this contentious plant. While the concept of chemovars has been proposed and has gained popularity of late, the lack of guidance in introducing this concept and the fact that chemovars are based on indirectly assessed traits with a heritable basis has likely impeded the implementation of the concept to a broader audience. Here I propose a simplified version of terpene hyper-classes based on three dominant terpenes that is shown to outperformed the classic indica-sativa-hybrid scheme of classification as well as a recently proposed terpene super-class scheme. This information was used to identify the most informative genetic markers for chemovar classification based on the terpene hyper-classes. I demonstrate the ability of clearly clustering accessions based on their dominant terpene and propose to extent this approach as a benchmark for chemovar classification in lieu of previously proposed models.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".