Influence of <i>Pythium oligandrum</i> Biocontrol on Fungal and Oomycete Population Dynamics in the Rhizosphere
Bibliographic record
Abstract
Fungal and oomycete populations and their dynamics were investigated following the introduction of the biocontrol agent Pythium oligandrum into the rhizosphere of tomato plants grown in soilless culture. Three strains of P. oligandrum were selected on the basis of their ability to form oospores (resting structures) and to produce tryptamine (an auxin-like compound) and oligandrin (a glycoprotein elicitor). Real-time PCR and plate counting demonstrated the persistence of large amounts of the antagonistic oomycete in the rhizosphere throughout the cropping season (April to September). Inter-simple-sequence-repeat analysis of the P. oligandrum strains collected from root samples at the end of the cropping season showed that among the three strains used for inoculation, the one producing the smallest amount of oospores was detected at 90%. Single-strand conformational polymorphism analysis revealed increases in the number of members and the complexity of the fungal community over time. There were no significant differences between the microbial ecosystems inoculated with P. oligandrum and those that were not treated, except for a reduction of Pythium dissotocum (ubiquitous tomato root minor pathogen) populations in inoculated systems during the last 3 months of culture. These findings raise interesting issues concerning the use of P. oligandrum strains producing elicitor and auxin molecules for plant protection and the development of biocontrol.
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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".