Investigating the impact of paclobutrazol and tannic acid on floral development of in vitro -grown cannabis plantlets
Bibliographic record
Abstract
Gibberellic acid (GA 3 ) is inhibitory to floral development of in vitro cannabis plants and inhibiting GA 3 biosynthesis promotes floral development. As such, paclobutrazol (PBZ), a potent GA 3 biosynthesis inhibitor may be useful for increasing floral biomass and expediting development, but due to health concerns, its use is prohibited in cannabis production. The present study was conducted to compare the use of PBZ with tannic acid (TA), a natural compound with potential GA 3 inhibiting characteristics. Results confirmed that PBZ significantly affected the number of flowers, percentage of flowering plantlet, and flower appearance time. Treatment using PBZ at a concentration of 10 μM resulted in the greatest number of flowers (7.95) compared to other treatments. Moreover, this compound at concentrations of 5 and 10 μM yielded the highest percentage of flowering plantlets, at 75 % and 70 %, respectively. Flowers also appeared 7–15 days sooner than other treatments. Additionally, the energy transfer efficiency in the photosynthetic system and chlorophyll concentration in plants treated with PBZ were considerably higher than those under other treatments. Under the PBZ treatment, the length of internode was significantly reduced. In contrast, TA generally had the opposite effect of PBZ, suggesting that it does not act as a GA 3 inhibitor in this context. Furthermore, positive effects of TA at a concentration of 10 μM were observed on total leaf area (840.08 mm 2 ) and stem length (40.09 mm). The highest number of leaves (12.5) was found in the presence of TA at a concentration of 100 μM. TA at its highest concentration (1000 μM) had an inverse effect on cannabis growth and flowering but was likely due to toxicity rather than any inhibitory effects. Consequently, the obtained results confirm the importance of growth regulators and natural compounds on plant growth and can broaden our understanding for future research and achievement of objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".