New issues and science in broiler chicken intestinal health: intestinal microbial composition, shifts, and impacts
Bibliographic record
Abstract
Intestinal health is important for maximising the health, welfare, and performance of poultry. In addition, intestinal health issues in poultry can have devastating financial impacts for producers, and food safety concerns for consumers. Until recently, intestinal health issues were seen as a handful of known infectious agents leading to a set of severe and identifiable named diseases. There is however an emerging area which depicts intestinal health as a more complex and multifaceted system than previously known. Recent progress in technology suitable for microbial community analysis has evolved our understanding of the chicken intestinal microbiome. It is now understood that shifts in the composition of microbial communities can occur. These shifts can result in a series of implications, including: disease, welfare, environmental, and food safety concerns. Minor shifts in intestinal microbial balance can result in a wide continuum of disease presentations ranging from severe to mild clinical, subclinical or asymptotic. Differential diagnosis of poultry intestinal health issues may be challenging and is important for applying appropriate treatment options. This review discusses new and emerging topics in broiler chicken intestinal health, with a focus on microbial composition, newly discovered microbial shifts in classical poultry diseases, range in severity of enteric diseases, newly identified organisms in normal intestinal flora, implications of shifts in intestinal microbial communities and diagnosis of emerging intestinal health issues in poultry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".