Plant growth regulatory effect and insecticidal activity of the extracts of the Tree of Heaven (Ailanthus altissima L.)
Bibliographic record
Abstract
BACKGROUND: There is an urgent need to explore and utilize naturally occurring products for combating harmful agricultural and public health pests. Secondary metabolites in the leaves of the Tree of Heaven, Ailanthus altissima L. have been reported to be herbicidal and insecticidal. The mode of action, however, of the active compounds in A. altissima are not understood. In this paper, we report the chemical characteristics of the herbicidal and insecticidal components in this tree, and will discuss the effect of light on the bioactivity of the active components. RESULTS: Extracts from the fresh leaves of A. altissima showed a strong plant germination/growth inhibitory effect in laboratory bioassays against alfalfa (Medicago sativa). The effect was dose-dependent. The growth inhibitory components were in the methylene chloride soluble fraction of the extract. The effect was greater in the light than in the dark. Other fractions had plant growth enhancing effect at lower concentrations. The extract was slightly insecticidal against yellow fever mosquito larvae (Aedes aegypti). CONCLUSIONS: The extract or its semi-purified fractions of A. altissima were strong plant growth inhibitors, therefore good candidates as potential environmentally safe and effective agricultural pest management agents. The finding that light affects the activity will be useful in the application of such natural products.
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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".