Cannabinoids and Terpenes: How Production of Photo-Protectants Can Be Manipulated to Enhance Cannabis sativa L. Phytochemistry
Bibliographic record
Abstract
Cannabis sativa L. is cultivated for its secondary metabolites, of which the cannabinoids have documented health benefits and growing pharmaceutical potential. Recent legal cannabis production in North America and Europe has been accompanied by an increase in reported findings for optimization of naturally occurring and synthetic cannabinoid production. Of the many environmental cues that can be manipulated during plant growth in controlled environments, cannabis cultivation with different lighting spectra indicates differential production and accumulation of medically important cannabinoids, including Δ 9 -tetrahydrocannabinol (Δ 9 -THC), cannabidiol (CBD), and cannabigerol (CBG), as well as terpenes and flavonoids. Ultraviolet (UV) radiation shows potential in stimulating cannabinoid biosynthesis in cannabis trichomes and pre-harvest or post-harvest UV treatment merits further exploration to determine if plant secondary metabolite accumulation could be enhanced in this manner. Visible LED light can augment THC and terpene accumulation, but not CBD. Well-designed experiments with light wavelengths other than blue and red light will provide more insight into light-dependent regulatory and molecular pathways in cannabis. Lighting strategies such as subcanopy lighting and varied light spectra at different developmental stages can lower energy consumption and optimize cannabis PSM production. Although evidence demonstrates that secondary metabolites in cannabis may be modulated by the light spectrum like other plant species, several questions remain for cannabinoid production pathways in this fast-paced and growing industry. In summarizing recent research progress on light spectra and secondary metabolites in cannabis, along with pertinent light responses in model plant species, future research directions 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".