Investigating the cecal microbiota of broilers raised in extensive and intensive production systems
Bibliographic record
Abstract
Abstract Background Intensive broiler production practices are structured to prevent the introduction and spread of pathogens; however, they can potentially minimize the exposure of broilers to beneficial commensal bacteria. In this study, we used 16s rRNA amplicon sequencing to perform a large-scale characterization of the cecal microbiota of 35-day-old broilers from intensive production systems (IPS) and from extensive production systems (EPS), aiming to determine which microbes are normal inhabitants of the broiler ceca and which microbes might be missing from broilers in IPS. In addition, we generated a collection of bacterial isolates to be used as a resource to further explore the effects of selected isolates on bird physiology, and to elucidate the role of individual bacterial species within the cecal microbial community.Results Our results indicated major differences in the microbiota of broilers between systems: the microbiota of broilers from EPS was dominated by Bacteroidetes, whereas Firmicutes dominated the microbiota of broilers from IPS. A number of bacterial taxa ubiquitous in the EPS microbiota were shown to be infrequent or absent from the IPS microbiota, and the EPS microbiota presented higher phylogenetic diversity and greater predicted functional potential than that of broilers in IPS.Conclusions In the current study, we identified Olsenella, Alistipes, Bacteroides, Barnesiella, Parabacteroides, Megamonas, and Parasutterella as core bacteria within the broiler microbiota that seem to be depleted in broilers from IPS, which could be further investigated for their effects on bird physiology and potential application as next-generation probiotics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".