Cannabinomics: Application of Metabolomics in Cannabis (Cannabis sativa L.) Research and Development
Bibliographic record
Abstract
Cannabis (Cannabis sativa L.) is a complex, polymorphic plant species, which produces a vast array of bioactive metabolites, the two major chemical groups being cannabinoids and terpenoids. Nonetheless, the psychoactive cannabinoid tetrahydrocannabinol (Δ9-THC) and the non-psychoactive cannabidiol (CBD), are the two major cannabinoids that have monopolized the research interest. Currently, more than 600 Cannabis varieties are commercially available, providing access to a multitude of potent extracts with complex compositions, whose genetics are largely inconclusive. Recently introduced legislation on Cannabis cultivation in many countries represents a great opportunity, but at the same time, a great challenge for Cannabis research and development (R&D) towards applications in the pharmaceutical, food, cosmetics, and agrochemical industries. Based on its versatility and unique capabilities in the deconvolution of the metabolite composition of complex matrices, metabolomics represents an ideal bioanalytical tool that could greatly assist and accelerate Cannabis R&D. Among others, Cannabis metabolomics or cannabinomics can be applied in the taxonomy of Cannabis varieties in chemovars, the research on the discovery and assessment of new Cannabis-based sources of bioactivity in medicine, the development of new food products, and the optimization of its cultivation, aiming for improvements in yield and potency. Although Cannabis research is still in its infancy, it is highly foreseen that the employment of advanced metabolomics will provide insights that will assist the sector to face the aforementioned challenges. Within this context, here, the current state-of-the-art and conceptual aspects of cannabinomics are presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".