The effect of silver nanoparticles on phytopathogenic spores of <i>Fusarium culmorum</i>
Bibliographic record
Abstract
The aim of the present experiment was to investigate the influence of silver nanoparticles on Fusarium culmorum (W.G. Smith) Sacc. (FC) spores. The silver nanoparticles were produced by the high-voltage arc discharge method. To test the effect of silver nanoparticles on FC spores, 3 parameters were tested. One of these parameters was the vegetative mycelial growth in 2 experiments. The first involved the growth of FC spores on potato dextrose agar (PDA) medium after contact with 0.12-10 ppm of silver nanoparticles, and the second the growth of spores after contact with 0.12-2.5 ppm solutions of silver, but with culturing on 3 types of media (PDA, nutrient-poor PDA, and agar) instead. The next parameter was the formation of spores after the mycelia were cultured. The last parameter was spore germination in a 2.5 ppm solution of silver nanoparticles. A significant reduction in mycelial growth was observed for spores incubated with silver nanoparticles. This relationship was dependent on the incubation time and type of growth medium, but did not depend significantly on the concentration of silver nanoparticles up to 2.5 ppm. The sporulation test showed that, relative to control samples, the number of spores formed by mycelia increased in the culture after contact with silver nanoparticles, especially on the nutrient-poor PDA medium. The 24 h incubation of FC spores with a 2.5 ppm solution of silver nanoparticles greatly reduced the number of germinating fragments and sprout length relative to the control.
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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) | 0.000 | 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".