Granite dust application to hemp – variety-specific impacts on growth and cannabinoid production
Bibliographic record
Abstract
The hemp industry has grown exponentially with the recent legalization of Cannabis sativa in Canada. With this new market expansion, there is an increased need for hemp plants, particularly for production of cannabinoids. Growing concerns regarding pesticide residues in commodities for human consumption, as well as global demand for fertilizer has increased consumer demand for natural products as alternatives to synthetic agrochemicals and pest management strategies. The objective of this study was to investigate the potential for using different composite granite dusts applied as soil amendments in improving C. sativa growth, and cannabinoid production (specifically, cannabidiol and cannabidiolic acid). We selected three varieties of industrial hemp with low yield production of cannabidiol (Fibranova, CFX-2, and Katani) and one variety with high yield production of cannabidiol (Cherry Blossom). Varieties were planted in potting soil amended with zero, five or ten percent granite dust mixture, and assayed for growth characteristics, and cannabinoid composition. Among tested cannabis varieties, results suggest that improvements to flower growth (> 44% mass) and cannabinoid production (> 2.5 fold or > 145%) from application of granite dust were evident in one variety of fibre hemp, CFX-2. Overall, this work suggests there may be selective benefits to soil applications of granite dust composites to improve hemp propagation, and that degree of improvement to cannabinoid production vary between varieties of hemp.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".