Cannabis Synthetic Seeds: An Alternative Approach for Commercial Scale of Clonal Propagation and Germplasm Conservation
Bibliographic record
Abstract
Indoor cannabis (Cannabis sativa) cultivation has been rapidly increasing in many countries after legalization. Besides conventional propagation through cuttings, synthetic seed production provides a competent system for mass propagation, germplasm conservation and international exchange of genetic materials. The present study developed a reliable protocol for cannabis synthetic seed production using encapsulation of nodal segments derived from in vitro or in vivo sources. Synthetic seeds were produced in 3% sodium alginate and 75 mM calcium chloride in Murashige and Skoog (MS) medium and stored under various environmental conditions for up to 150 days. The plantlets regrowth efficiency was monitored on culture media up to 30 days after the storage period. Regrowth rates of 70% and 90% were observed in synthetic seeds from in vitro and in vivo-derived sources, respectively, when stored in 6 °C under 50 μmol s−1 m−2 light for 150 days. Furthermore, addition of acetylsalicylic acid (ASA) to the encapsulation matrix not only postponed precocious germination of synthetic seeds at 22 °C, but also improved the regrowth rate of in vivo-derived synthetic seeds to 100% when they were stored in 6 °C under light. Exposure to light during storage significantly increased shoot length of regrown synseeds when compared to those stored in darkness. This difference in shoot growth disappeared when synseeds were treated with 25 µM ASA. All regenerated plantlets were rooted and acclimatized in sterile rockwool plugs without morphological changes.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".