UV LED disinfection as a novel treatment for common salmonid pathogens
Bibliographic record
Abstract
Aeromonas salmonicida and Yersinia ruckeri are common pathogenic bacteria that impact salmonid aquaculture. Although vaccinations are available against both organisms, large-scale vaccination efforts can be expensive, cumbersome, and are not always reliable. Alternatively, these pathogens have been effectively inactivated using UV radiation from mercury-based systems. These systems are energy intensive and fragile which currently limits their use to closed and semi-closed production systems. UV light emitting diodes (UV LEDs) have recently emerged as a novel alternative to traditional mercury-based treatment. UV LEDs have durable housing, a relatively low energy draw, can be powered by a battery source and are adaptable to challenging environments. This study examined the effectiveness of three UV LED wavelengths for disinfection of A. salmonicida and Y. ruckeri in pure culture and resuspended in a wastewater matrix. All tested UV LEDs were effective in disinfecting both organisms. 267 and 279 nm wavelengths outperformed 255 nm disinfection in both test matrices. Particulate matter from wastewater reduced the upper limit of treatment for A. salmonicida but results still indicated that all wavelengths were effective for disinfection in a challenging matrix. This study represents the first use of UV LEDs for disinfection of A. salmonicida and Y. ruckeri and provides impact to aquaculture producers looking to implement novel technologies for disease 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) | 0.001 | 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".