Predicting chemovar cluster and variety verification in vegetative cannabis accessions using targeted single nucleotide polymorphisms
Bibliographic record
Abstract
The cannabis industry has gained momentum and global acceptance recently, culminating in the legalization of adult use at the federal level in Canada, a first among G20 countries. Inherent to legalization, a highly regulated regime has emerged, mostly centered on end user safety, restriction of access to youth, and diversion of market shares away from the black market and organized crime. The lack of authentication of cannabis varieties remains as an issue often unaddressed by the regulators, although this has the potential to seriously hamper research and the medical application of cannabis derived products. Here, we extend upon previous work that aims to classify cannabis accessions based on their dominant terpene profiles, focusing on four main informative terpenes, beta-myrcene, terpinolene, limonene and beta-caryophyllene. We identify three major terpene groups and present a simple genetic-based tool to bridge the variety identification gap and to enable the prediction of terpenoid expression in vegetative cannabis. This genetic tool offers promise to sorting out the strain name game that has been ongoing, thus providing greater transparency in the industry and contributing to an enhanced understanding of cannabis medicine for the end user.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".