Analysis and Comparison of Gut Microbiome in Young Detection Dogs
Bibliographic record
Abstract
The detection dogs are well-known for their excellent capabilities to sense different kinds of smells, which can play an important role in completing various searching and rescuing missions. The recent studies have demonstrated that the excellent olfactory function of detection dogs might be related with the gut microbes via the bidirectional communications between the gastrointestinal tract and the brain. In this study, the gut microbial communities of three types of breeds of detection dogs (Springer Spaniel, Labrador Retriever, and German Shepherd) were studied and compared. The results revealed that the richness and the diversity of gut microbiome German Shepherd dogs were significantly higher than the Labrador Retriever dogs and the Springer Spaniel dogs. At the phylum level, the most predominant gut microbial communities of the detection dogs were comprised of Fusobacteriota, Bacteroidetes, Firmicutes, Proteobacteria, Campilobacterota, and Actinobacteriota. At the genus level the most predominant gut microbial communities were comprised of Fusobacterium, Megamonas, Prevotella, Alloprevotella, Bacteroides, Haemophilus, Anaerobiospirillum, Helicobacter, Megasphaera, Peptoclostridium, Phascolarctobacterium, and Streptococcus. However, the gut microbial communities of the three dogs group were also obviously different. The mean relative abundance of Fusobacterium, Prevotella, Alloprevotella, Megamonas, Bacteroides, and Phascolarctobacterium presented significant differences in the three groups. According to the portraits and characteristics of the gut microbiome in young detection dogs, multiple kinds of nutritional interventions could be applied to manipulate the gut microbiota, with the aim of improving the health states and the olfactory performances.
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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".