Effects of exogenous substances on saponin content in root of Polygala tenuifolia Willd
Bibliographic record
Abstract
【Objective 】The effects of exogenous substances on the saponin content in root of Polygala tenuifolia Willd were studied to provide references for improving the medicinal ingredients of planting of Polygala tenuifolia Willd. 【Method】During the leaf-expansion period, naphthalene acetic acid(25,50,100 mg/L), gibberellin(50,150,300mg/L), cytokinin(100, 200, 300 mg/L),abscisic acid(5, 10, 15 mg/L), methyl jasmonate(200, 300, 400 μmol/L)and salicylic acid(50, 100, 200 mg/L)were sprayed respectively on the leaf surface, and the effects of the different concentrations of exogenous substances on saponin content in root of the field-planting Polygala tenuifolia Willd were compared. 【Result】The low, medium, and high concentrations of four kinds of exogenous hormone, naphthalene acetic acid, gibberellin, cytokinin and abscisic acid could enhance the saponins in root of Polygala tenuifolia Willd to 4.07,3.98, 3.91 times; 22.33, 14.21, 13.72 times; 7.8, 6.2, 5.4 times; 2.1, 2.3, 2.6 times respectively, compared to the control. Low, medium, and high concentrations of two kinds of elicitors of methyl jasmonate and salicylic acid could enhance the content of saponin to 6.6, 14.2, 7.6 times and 1.65, 2.95, 3.49 times respectively, compared to the control. The effecting order of 6 kinds of exogenous substances on the saponins accumulation was gibberellin methyl jasmonate cytokinin naphthalene acetic acid salicylic acid abscisic acid. 【Conclusion 】Exogenous substances could significantly improve the saponin content in root of Polygala tenuifolia Willd by spraying on leaf surface, and this method could be applied in production practice.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".