Effects of gypsophila saponins on bacterial growth kinetics and on selection of subterranean clover rhizosphere bacteria
Bibliographic record
Abstract
Plant secondary metabolites, such as saponins, have a considerable impact in agriculture because of their allelopathic effects. They also affect the growth of soil microorganisms, especially fungi. We investigated the influence of saponins on rhizosphere bacteria in vitro and in soil conditions. The effects of gypsophila saponins on the growth kinetics of rhizosphere bacteria were studied by monitoring the absorbance of the cultures in microtiter plates. Gypsophila saponins (1%) increased the lag phase of bacterial growth. The impact of gypsophila saponins on subterranean clover rhizosphere was also investigated in a pot experiment. The addition of gypsophila saponins did not modify clover biomass but significantly increased (twofold with 1% saponins) the weight of adhering soil. The number of culturable heterotrophic bacteria of the clover rhizosphere was not affected by the addition of gypsophila saponins. Nevertheless, the phenotypical characterization of the dominant Gram-negative strains of the clover rhizosphere, using the Biolog system, showed qualitative and quantitative differences induced by 1% saponins. With the addition of saponins, the populations of Chryseomonas spp. and Acinetobacter spp., the two dominant culturable genera of control clover, were no longer detectable or were significantly decreased, while that of Aquaspirillum dispar increased and Aquaspirillum spp. became the major genus. Aquaspirillum dispar and Aquaspirillum spp. were also the dominant rhizosphere bacteria of Gypsophila paniculata, which greatly accumulates these saponins in its roots. These results suggest that saponins may control rhizosphere bacteria in soil through rhizodeposition mechanisms.
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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".