Flower Power: Floral reversion as a viable alternative to nodal micropropagation in <i>Cannabis sativa</i>
Bibliographic record
Abstract
Abstract The legalization of Cannabis sativa L. for recreational and medical purposes has been gaining global momentum, leading to a rise in interest in Cannabis tissue culture as growers look for large-scale solutions to germplasm storage and clean plant propagation. Mother plants used in commercial propagation are susceptible to insect pests and disease and require considerable space. While micropropagation can produce disease free starting material in less space, current published in vitro micropropagation methods are not robust and few report high multiplication rates. Further, these micropropagation methods rely on photoperiod-sensitive plants which can be maintained in a perpetual vegetative state. Current methods are not adaptable to long-term tissue culture of day-neutral cultivars, which cannot be maintained in perpetual vegetative growth. In this study, we chose to develop a micropropagation system which uses C. sativa inflorescences as starting materials. This study used two cannabis cultivars, two plant growth regulators (PGR; 6-benzylaminopurine and meta-topolin) at different concentrations, and two different numbers of florets. Here we show that floral reversion occurs from meristematic tissue in C. sativa florets and that it can be used to enhance multiplication rates compared to existing in vitro methods. Floret number was shown to have a significant impact on percent reversion, with pairs of florets reverting more frequently and producing healthier explants than single florets, while cultivar and PGR had no significant effect on percent reversion. Compared with our previously published nodal culture studies, the current floral reversion method produced up to eight times more explants per tissue culture cycle. Floral reversion provides a foundation for effective inflorescence-based micropropagation systems in C. sativa .
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".