Pre- and post-storage applications of rhamnolipids and cyprodinil + fludioxonil against <i>Aspergillus</i> section <i>Nigri</i> population and ochratoxin A contamination of grapes
Bibliographic record
Abstract
Grapevines constitute a very important crop in Greece and ochratoxin A (OTA) contamination of grapes is one of the most serious problems. The main aim of this study was to investigate the fungicidal activity of the fungicide Switch 62.5WG (37.5% v/v cyprodinil + 25% v/v fludioxonil) and Rhapynal (80% rhamnolipids) to reduce the black Aspergillus spp. (section Nigri) population and OTA contamination of grapes grown in Greece. Applications with the above products were conducted both at the harvesting stage and after cold storage. The results showed that pre-harvest applications with Switch 62.5WG or Switch 62.5WG + Rhapynal generally reduced the black Aspergillus spp. population, but not the OTA contamination of grapes. However, a repeated application after cold storage further reduced the black Aspergillus spp. population and significantly reduced the OTA contamination of grapes. Generally, this study showed that it is possible to reduce the black Aspergillus spp. population, but not the ochratoxin A contamination of grapes by spray applications with the fungicide Switch 62.5WG. However, fungicide application to grapes immediately after cold storage (2 months) could reduce ochratoxin A contamination of grapes and further reduce the black Aspergillus spp. population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".