Identification and Bioassay of Allelopathic Substances from Plant and Rhizosphere Soil Extracts of Adzuki Bean (Vigna angularis [Willd.] Ohwi & H. Ohashi.)
Bibliographic record
Abstract
The soil sickness of Adzuki bean [Vigna angularis (Willd.) Ohwi & H. Ohashi] have been getting attention seriously under a continuously mono-cropping management-system in China. To investigate the allelopathy of rhizosphere soil and plant of Adzuki bean, culture dish and pot experiments were conducted. The compounds in the products were identified by High Performance Liquid Chromatography (HPLC).The aqueous extracts from rhizospheric soil and whole plant inhibited seed index (seed germination and seed potential) and plant growth under different degrees, and it showed concentration gradient effect on the inhibition of seed germination. Inhibition was stronger with increasment in extract concentration with the highest effect at 0.16 parts aqueous extract to 1 parts deionized water (0.16g·mL-1). The impact on growth varied among aqueous extracts from rhizospheric soil and the whole plant. Five phenolic compounds, cinnamic, phthalic, p-hydroxybenzoic, and isobutyricand glutaric acids, were detected from the rhizosphere soil and plant extract of Adzuki bean by HPLC method. Cinnamic, phthalic, and p-hydroxybenzoicacids were higher than that of the other phenolic acids. This result suggests that autotoxicity effects of phenolics on Adzuki bean probably happen in continuous mono-cropping systems.
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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) | 0.001 | 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".