Diet affects the composition and diversity of the Mammalian gut microbiota
Bibliographic record
Abstract
The mammalian gut microbiota is colonized by a complex and dynamic population of microbes, and has been shown to play a substantial role in mediating health and disease in individuals. Although the gut microbiome is subjected to a wide variety of host and environmental factors that influence microbial composition, diet is considered as one of the main drivers involved in shaping the gut microbiome structure. The composition of the mammalian gut microbiota has been shown to correlate with the secretion of specialized enzymes used to metabolize distinct substrates across the three main diet categories: carnivory, omnivory, and herbivory. Herbivores have been shown to have the greatest microbial diversity, driven by the need for microbial assemblages to break down recalcitrant plant fibres into usable energy. In this study, we analyzed alpha and beta diversity from a data set composed of 41 mammalian species spanning across 6 orders. We confirm previous results that mammals belonging to different diet types harbour distinctly different gut microbial communities. We also show that certain microbial families are associated with each of the three diet categories through indicator taxonomy analysis. Furthermore, the relationship between the proportion of plants in the diet and the gut microbial composition in omnivores is inconclusive. Collectively, these results allowed us to further our understanding into the effects of diet on the mammalian gut microbiota.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".