Rhizosphere persistence of three Pythium oligandrum strains in tomato soilless culture assessed by DNA macroarray and real-time PCR
Bibliographic record
Abstract
In tomato soilless culture, plant-disease optimal control and growth promotion are achieved when the rhizosphere is heavily colonized by the biocontrol agent Pythium oligandrum. Discrepancies in performance are generally attributed to the poor persistence of P. oligandrum on roots. In this study, three selected strains of P. oligandrum were introduced into the rhizosphere of greenhouse-grown tomato plants, and their persistence was assessed by DNA macroarray hybridization and real-time PCR. The experimental data from DNA detection and plate counting were compared. PCR-based methods detected P. oligandrum throughout the 6-month growing season, whereas plate counting indicated its presence only over the first 3 months. Moreover, the DNA array method provided information about the various Pythium species present in the rhizosphere: P. dissotocum was frequently detected on roots of plants, without distinction between plants inoculated or not inoculated with the antagonist. The detection of other Pythium species was noticed sporadically (P. ultimum, P. sylvaticum and P. intermedium), independent of the treatment. Even though the yield enhancement is not significant throughout the entire growing season, data obtained from epidemiological studies demonstrate an enhancement of P. oligandrum persistence on the rhizosphere of plants and less use of mycoparasitism.
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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".