The Terroir of Cannabis: Terpene Metabolomics as a Tool to Understand Cannabis sativa Selections
Bibliographic record
Abstract
Abstract The phytochemical diversity of Cannabis chemovars is not well understood, and many chemovars were created in informal breeding programs without records of parentage or the criteria for selection. Key criteria for selection sometimes included aroma notes and visual cues, which some breeders associated with pharmacological activity. We hypothesized that the process of selection for scents believed to be related to specific tetrahydrocannabinol levels has resulted in modified terpene biosynthesis in these chemovars. Thirty-two cannabinoids, 29 monoterpenes and 38 sesquiterpenes were measured in 33 chemovars from 5 licensed producers. A classification system based on cannabinoid content was used with targeted metabolomic tools to determine relationships in the phytochemistry. Three monoterpenes, limonene, β-myrcene, and α-pinene, and two sesquiterpenes, caryophyllene and humulene, were abundant in the majority of chemovars. Nine terpenes were present in tetrahydrocannabinol-dominant chemovars. Three monoterpenes and four sesquiterpenes were predominantly found in cannabidiol-containing chemovars. Low abundance terpenes may have been the aromatic cues identified by breeders. The medicinal activity of some of the terpenes is likely to contribute to the pharmacological effect of specific chemovars. Together, these data demonstrate the synergy of compounds in Cannabis chemovars and point to the need for additional research to understand the phytochemical complexity.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".