Lactobacillus elicits a 'Marmite effect' on the chicken cecal microbiome
Bibliographic record
Abstract
Abstract The poultry industry has traditionally relied on the use of antibiotic growth promoters (AGPs) to improve production efficiency and minimize infection. With the recent drive to eliminate the use of AGPs, novel alternatives are urgently required. Recently attention has turned to the use of synthetic communities that may be used to ‘seed’ the developing microbiome. The current challenge is identifying keystone taxa whose influences in the gut can be leveraged for probiotic development. To help define such taxa we present a meta-analysis of 16S rRNA surveys of 1572 cecal microbiomes generated from 19 studies. Accounting for experimental biases, consistent with previous studies, we find that AGP exposure can result in reduced microbiome diversity. Network community analysis defines groups of taxa that form stable clusters and further reveals Lactobacillus to elicit a polarizing effect on the cecal microbiome, exhibiting relatively equal numbers of positive and negative interactions with other taxa. Our identification of stable taxonomic associations provides a valuable framework for developing effective microbial consortia as alternatives to AGPs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".