Salmonid Antibacterial Immunity: An Aquaculture Perspective
Bibliographic record
Abstract
The aquaculture industry is continuously threatened by infectious diseases, including those of bacterial origin. Regardless of the disease burden, aquaculture is already the main method for producing fish protein, having displaced capture fisheries. One attractive sector within this industry is the culture of salmonids, which are (a) uniquely under pressure due to overfishing and (b) the most valuable finfish per unit of weight. There are still knowledge gaps in the understanding of fish immunity, leading to vaccines that are not as effective as in terrestrial species, thus a common method to combat bacterial disease outbreaks is the use of antibiotics. Though effective, this method increases both the prevalence and risk of generating antibiotic-resistant bacteria. To facilitate vaccine design and/or alternative treatment efforts, a deeper understanding of the teleost immune system is essential. This review highlights the current state of teleost antibacterial immunity in the context of salmonid aquaculture. Additionally, the success of current techniques/methods used to combat bacterial diseases in salmonid aquaculture will be addressed. Filling the immunology knowledge gaps highlighted here will assist in reducing aquaculture losses in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".