Bacteriology of the Labrador dog gut: a cultural and genotypic approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it