Fishing for the right probiotic: host–microbe interactions at the interface of effective aquaculture strategies
Bibliographic record
Abstract
Effective aquaculture management strategies are paramount to global food security. Growing demands stimulate the intensification of production and create the need for practices that are both economically viable and environmentally sustainable. Importantly, pathogenic microbes continue to be detrimental to fish growth and survival. In terms of host health, the intestinal mucosa and its associated consortium of microbes have a critical role in modulating fitness and present an attractive opportunity to promote health at this interface. In light of this, the administration of probiotic microorganisms is being considered as a means to restore and sustain health in fish. Current evidence suggests that certain probiotic strains might be able to augment immunity, enhance growth rate and protect against infection in salmonids, the most economically important family of farmed finfish. This review affirms the relevance of host-microbe interactions in salmonids in light of emerging evidence, with an emphasis on intestinal health. In addition, the current understanding of the mode of action of probiotics in salmonid fish is discussed, along with delivery systems that can effectively carry the living microbes.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".