Bibliographic record
Abstract
Cannabis sativa (cannabis, marijuana, hemp) is a plant species grown widely for its psychoactive and medicinal properties. Cannabis products were made illegal in most of the world in the early 1900s, but regulations have recently been relaxed or lifted in some jurisdictions, notably Canada and parts of the United States. Cannabis is usually grown for the resin produced in trichomes on the flowers of female plants. The major components of that resin are isoprenoids: cannabinoids, monoterpenes, and sesquiterpenes. Terpene profiles in cannabis flowers can vary widely between cultivars. My research addresses the genomic underpinnings and biochemical mechanisms of terpene and cannabinoid biosynthesis in cannabis, and patterns of terpene accumulation between organs, developmental stages, and cultivars. Using metabolite profiling, I demonstrated that terpenes accumulate in floral trichomes over the course of development, and that terpene profiles in trichomes differ based on tissue and developmental stage. In this thesis, I describe the terpene profiles of seven cannabis cultivars. I identified and characterized 29 terpene synthase (TPS) genes and their encoded enzymes and describe the relationship between TPS expression and metabolite profiles. I describe trichome-specific transcriptomes for five cultivars and identify highly expressed genes common to cannabis trichomes. I also identified and describe an aromatic prenyltransferase responsible for biosynthesis of cannabigerolic acid, the branch-point intermediate in cannabinoid biosynthesis. Collectively, this thesis comprises a broad and detailed characterization of specialized isoprenoid biosynthesis in cannabis. The results provide new insights into mechanisms of terpene and cannabinoid biosynthesis, and the roles of different enzymes in determining the metabolite complement of cannabis trichomes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".