The Role of Agriculture in Supplying Nutritional, Medicinal, and Recreational Cannabis Products
Bibliographic record
Abstract
In past centuries, the cannabis plant was one of the world's most admired crops, furnishing a range of indispensable goods. For most of the last century, however, fear of its abuse potential suppressed almost all legal cultivation. Recently, the constraints limiting cannabis commercialization have been loosened, a tidal wave of research and development has been unleashed, and cannabis is becoming a trillion dollar industry. In the last two decades, Canada has become the world leader in production of hempseed food preparations and nutritional oilseed extracts based on non-euphoric (“industrial”) varieties, and there is enormous potential to breed improved cultivars that can be employed to produce a range of functional foods and nutraceuticals. Euphoric strains currently cultivated for marijuana are far less well developed than industrial varieties, and require modern breeding for efficient harvest of the cannabinoids. The best known cannabinoid is the euphoric THC, but the non-euphoric cannabinoids, particularly CBD, have considerable medicinal potential, and their agricultural production also requires development. Marijuana production in Canada is currently based on indoor cultivation, which provides security but is very expensive and wasteful of energy. The most pressing short-term need is breeding of improved varieties, particularly short-stature (so-called “dwarf”) cultivars, in the manner that most other crops have been altered in recent decades, to greatly increase efficient production. The most pressing long-term need is assembly of a public permanent germplasm (seed) collection which will preserve vanishing genetic resources of cannabis plants and provide the essential basis for breeding both industrial and medicinal cannabis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".