Eliciting increased flavonoids content in <i>Ginkgo biloba</i> leaves through exogenous salicylic acid and methyl jasmonate treatments
Bibliographic record
Abstract
Prior to Ginkgo leaf harvest, we implemented four exogenous applications of salicylic acid (SA: 50, 80, and 100 mg·L−1) or methyl jasmonate (MJ: 20, 40, and 80 mg·L−1) as leaf spray to investigate their effect on flavonoids content and related enzyme activity. Compared with the control, a significant increase of 12% and 26% in flavonoid content was observed 7 days after the first application of 100 mg·L−1 SA and 20 mg·L−1 MJ, respectively; both were determined to be the best treatments. Operationally, administering either elicitor (SA or MJ) 1 week prior to leaf harvest is deemed to be the most practical and economic option. Additionally, enzymes activity involved in flavonoid metabolism (phenylalanine ammonia-lyase, cinnamate 4-hydroxylase, and 4-coumarate:coenzyme A ligase) and antioxidants (peroxidase, superoxide dismutase, and catalase) significantly increased in most SA and MJ treatments. The results strongly support Ginkgo leaf response to SA- or MJ-induced stress through increased nonenzymatic antioxidant compounds (flavonoids) and activity of antioxidant enzymes.
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 | 0.000 | 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".