Appropriate Chicken Sample Size for Identifying the Composition of Broiler Intestinal Microbiota Affected by Dietary Antibiotics, Using the Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis Technique
Bibliographic record
Abstract
The bacterial microbiota in the broiler gastrointestinal tract are crucial for chicken health and growth. Their composition can vary among individual birds. To evaluate the composition of chicken microbiota in response to environmental disruption accurately, 4 different pools made up of 2, 5, 10, and 15 individuals were used to determine how many individuals in each pool were required to assess the degree of variation when using the PCR-denaturing gradient gel electrophoresis (DGGE) profiling technique. The correlation coefficients among 3 replicates within each pool group indicated that the optimal sample size for comparing PCR-DGGE bacterial profiles and downstream applications (such as identifying treatment effects) was 5 birds per pool for cecal microbiota. Subsequently, digesta from 5 birds was pooled to investigate the effects on the microbiota composition of the 2 most commonly used dietary antibiotics (virginiamycin and bacitracin methylene disalicylate) at 2 different doses by using PCR-DGGE, DNA sequencing, and quantitative PCR techniques. Thirteen DGGE DNA bands were identified, representing bacterial groups that had been affected by the antibiotics. Nine of them were validated. The effect of dietary antibiotics on the microbiota composition appeared to be dose and age dependent. These findings provide a working model for elucidating the mechanisms of antibiotic effects on the chicken intestinal microbiota and for developing alternatives to dietary antibiotics.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".