Effect of early gut microbiota intervention using pre-designed poultry microbiota substitute on broiler health and performance
Bibliographic record
Abstract
Context The designer gut microbiota in broiler chickens is a novel concept involving post-hatch inoculation of chicks with beneficial or commensal non-pathogenic bacteria as an inoculum. This process aims to control gut colonisation by administering desirable microbiota to prevent access to harmful and pathogenic bacteria via competitive exclusion. Aims This study aimed to assess the impact of one such intervention on broiler gut microbiota, microbial diversity and growth performance. Methods The intervention involved spraying the newly hatched chicks with a commercially available mix of non-pathogenic bacterial species isolated from chicken intestine. Key results Bodyweight gain was significantly higher in the treated group, and performance measures showed improvement. Beta diversity analysis showed a significant difference in the gut microbiota between the control and treatment groups. Conclusions The study demonstrated the effects and potential benefits of early intervention to influence gut microbial composition and improve the uniformity across the flock and enhance broiler health and performance. Implications This study has highlighted the complexity of microbiota dynamics and the need for further research to fully understand the implications of designer gut microbiota in poultry production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".