Redspotted Grouper Nervous Necrosis Virus and the Reassortant RGNNV/SJNNV In Vitro Susceptibility against a Commercial Peroxy-Acid Biocide under Different Conditions of Use
Bibliographic record
Abstract
Aquaculture is a constantly growing sector. The intensification of fish production and the movement of aquatic animals could cause the spread of infectious diseases. Remarkably, the diffusion of viral agents represents the major bottleneck for finfish production, and viral encephalopathy and retinopathy (VER) is considered the most impacting disease for Mediterranean aquaculture. No effective therapies are available to contrast VER, and vaccination can be applied only in grow-out facilities. Hence, programs to minimize the sanitary risks in farms are paramount to implementing hygienic standards and biosecurity. This study aimed to evaluate the in vitro virucidal activity of a peroxy-acid disinfectant (Virkon® S, DuPont, Sudbury, UK) towards the two NNV strains most widespread in the Mediterranean Sea. Remarkably, two protocols were applied to assess the virucidal activity under different conditions of use: the suspension test and the net test. The latter has been applied to evaluate the efficacy of the biocide on instruments, simulating the in-field application. The obtained results demonstrated the suitability of the tested biocide for NNV inactivation, being effective under some of the tested conditions. However, the presence of organic matter, the concentration of the product, and the application conditions can significantly affect the result of the disinfection procedure.
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".