396 Gaps and tips in the development of probiotics for swine production
Bibliographic record
Abstract
Abstract Probiotics have long been used in food animal production to improve animal health and nutrition. This practice has received an extensive interest after European Union countries restricted the use of antibiotics as animal growth promoters (AGP). Although probiotics have good potential to replace AGPs by offering various benefits to animal hosts in general, their efficacy and consistency remain to be a challenge in application. Developing scientific evidence-based probiotics is thus critical to resolve the issue. The current presentation points out the major gaps in the development and application of probiotics and is focused on the tips that can be used to select novel probiotic isolates with efficacy and specificity. These have been illustrated by the authors’ research. For instance, the efficiency of probiotic selection is largely limited by the use of farm animals and how a lab animal model can be used to speed up the selection and reveal the molecular mechanisms underlying probiotic effects. The potential of advanced genomic approaches in identifying such probiotics and determining their molecular mechanisms is also discussed using an unique example in the field, which is relevant to the control of diarrhea in piglets.
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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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".