A Zoospore Inoculation Method with <i>Phytophthora sojae</i> to Assess the Prophylactic Role of Silicon on Soybean Cultivars
Bibliographic record
Abstract
The objective of this study was to evaluate whether silicon (Si) amendments, known to have a prophylactic role against biotrophic and hemibiotrophic pathogens, could protect soybean against Phytophthora sojae. To fulfill this objective, the initial challenge was to develop a method of inoculation that reproduced the natural infection process while allowing regular Si feeding to the plants. In a first set of experiments, inoculation of P. sojae zoospores directly into hydroponic solutions led to reproducible infections and expected phenotypes when using 'Williams' (rps), 'L75-6141' (Rps1a), 'haro15' (Rps1k), and 'L77-1863' (Rps1b) soybean challenged to races 3 and 7 of P. sojae. This approach offers the advantage of testing simultaneously many soybean cultivars against different races of P. sojae in a controlled environment, and the expression of partial and root resistance. In a second set of experiments aimed at testing the effect of Si, our results clearly showed that Si amendments had a significant effect on disease reduction and plant yield. The effect was particularly noticeable when combined with a cultivar displaying a certain level of resistance to the disease. These results demonstrate a useful method of direct inoculation of soybean plants with P. sojae zoospores through a hydroponic system and show that Si amendments can represent an alternative method of control of P. sojae against soybean.
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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 | 0.001 |
| 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".