Salmonid Antibacterial Immunity: An Aquaculture Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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 it