Impact of FortiPhi-S bacteriophage solution on the environmental microbiome in poultry litter systems from commercial operations
Bibliographic record
Abstract
The uprising demand of poultry products has led to an increase in the production of chickens. Nonetheless, this increase also gives way for an uprise in different types of issues such as food safety, human and animal health. While postharvest intervention strategies are considerably studied and established, preharvest food safety is considered more challenging. Therefore, anti-microbials like antibiotics are commonly used in poultry production to address and prevent contamination by pathogens. As a result of the many drawbacks associated with antibiotics, there is a growing demand for alternatives in animal production. Consequently, the use of bacteriophages in this field has been on the rise. This study highlights the effect of the application of the bacteriophage treatment FortiPhi-S on commercial poultry litter at different concentrations. The results demonstrate a significant difference by decreasing the richness of samples and increasing the diversity. The treatment also reduced the pathogenic families Staphylococcaceae and maintaining beneficial families such as Lachnospiraceae and Bacteroidaceae. Furthermore, pathogenic strains of Salmonella, Clostridia, and Escherichia-Shigella were significantly reduced or eliminated. The results demonstrated that the bacteriophage treatment FortiPhi-S has a significant effect on the microbial composition and diversity of poultry litter.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".