Fishing for nutrient-competing antagonists for ginseng pathogen control via root biomass
Bibliographic record
Abstract
Phytopathogenic fungi are primarily responsible for destructive plant diseases that threaten food security. Biological control agents are generally based on their antibiotic characteristics. Nutrient competition is typical in microbes; however, the use of nutrient-competing antagonists for plant disease control remains underutilized. We found that ginseng root biomass selectively enriches soil pathogens and that biomass depletion prevents pathogen accumulation. We developed a method to capture specific ginseng root biomass-decomposing fungi from soils. We obtained three fungi via this method: one typical ginseng pathogen, Fusarium oxysporum , and two nonpathogenic fungi. These fungi do not display antibiosis to each other. However, nonpathogenic fungi significantly prevent ginseng root biomass-mediated accumulation of F. oxysporum . In addition, all three of these fungi inhibit the changes in the soil fungal community mediated by ginseng root biomass. To validate pathogen inhibition and community manipulation, we tested the effects of a commercial biomass-decomposing fungus, Aspergillus oryzae , on F. oxysporum accumulation and soil fungal community alteration after the addition of ginseng root mixture. The results support our conclusion that this method is simple and effective. Our results highlight an innovative application of nutrient-competing antagonists for plant disease control and a convenient protocol for screening for nutrient-competing antagonists.
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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".