Effects of ferulic and <i>p</i>-hydroxybenzoic acids on <i>Fusarium</i> community structure and abundance in cucumber seedling rhizosphere
Bibliographic record
Abstract
Soil microorganisms play an important role in agricultural ecosystem. However, there is little information about the effects of putative allelochemicals on specific soil microorganisms in vivo. Cucumber seedlings were treated with four concentrations of ferulic and p-hydroxybenzoic acids (0–1.0 μmol·g−1 soil) in soil. Effects of ferulic and p-hydroxybenzoic acids on rhizosphere Fusarium community structures and abundance were analyzed by polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) and real-time PCR techniques, respectively. The results showed that ferulic acid at concentrations of 0.25, 0.5, and 1.0 μmol·g−1 soil significantly reduced the number of bands of Fusarium, and ferulic acid at concentrations of 0.5 and 1.0 μmol·g−1 soil significantly reduced Shannon–Wiener and evenness index of Fusarium community. All concentrations of p-hydroxybenzoic acid changed the community structure of Fusarium, including decreasing the number of bands, Shannon–Wiener, and evenness index. Ferulic and p-hydroxybenzoic acids at all concentrations significantly promoted the abundance of Fusarium. Ferulic and p-hydroxybenzoic acids at 0.5 μmol·g−1 soil had the highest Fusarium abundance among all treatments. These results indicate that four concentrations of ferulic acid had various effects on Fusarium, which was different from p-hydroxybenzoic acid, and this may be related to the cucumber autotoxicity, giving us a further understanding of soil sickness.
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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".