Bacteriology of the Labrador dog gut: a cultural and genotypic approach
Bibliographic record
Abstract
AIMS: To carry out an extensive study of the microflora composition of the Labrador dog gut. METHODS AND RESULTS: Faecal specimens from four Labradors were collected and plated onto growth media designed to recover total anaerobes, bacteroides, bifidobacteria, lactobacilli, clostridia, Gram-positive cocci, total aerobes and coliforms. Morphologically different isolates were collected from all agars inoculated with faeces from one canine individual (repeated four times). A total of 157 out of 171 isolates were identified using 16S rRNA gene sequencing. Sequence analysis showed that agar selectivity was poor, especially when bacteroides and Gram-positive cocci were the targets. Bifidobacteria were not detected in any of the samples analysed, indicating their presence at low or negligible levels. The gene sequences of many of the isolates (n=45, representing 29% of the total) did not correlate with known species in the Ribosomal Database Project and EMBL databases, suggesting the presence of novel gut diversity. CONCLUSIONS: Traditional culture methods fail to reflect the bacterial diversity present in Labrador dog faeces. SIGNIFICANCE AND IMPACT OF THE STUDY: This study has shown the value of molecular-based methodologies for determining bacterial profiles in the Labrador dog gut microbiota, but has also exposed the limitations of purportedly selective agars.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".