Exploring the breadth and depth of diversity within the canine gut microbiome
Bibliographic record
Abstract
The mammalian gut microbiota is an essential factor in intestinal function and thus overall health. In the post genomic era, culture independent studies into the gut microbiota, particularly that of humans have allowed great leaps forward in knowledge of a once cryptic ecosystem. Furthermore, recent advances in sequencing technologies have allowed acceleration and broadening of work in this research field. Despite this, the canine gut microbiome has remained relatively uncharacterised. This work investigates the faecal microbiota of a diverse multi-breed and multi-location group of 79 dogs, by amplifying and sequencing the 16S rDNA from these dogs using both Sanger sequencing of clone libraries and high throughput pyrosequencing. A robust census of the canine faecal microbiota was undertaken. The most abundant genera were the Bacteroides, Prevotella, Cetobacterium, Fusobacterium, Sutterella and Megamonas. A limited core microbiome was defined in 90% of the study population; this represented less than 0.5% of richness but more than 37.4% of abundance. Influences of host sex, diet and age were investigated but were found not significant. Some evidence was found for breed associated richness differences, most marked in Labrador retrievers and miniature Schnauzers. Furthermore, the microbiota of the Labradors appeared to cluster separately from the other breeds.
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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 | 0.001 | 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".