Comparative analysis of fecal microbiota and intestinal microbial metabolic activity in captive polar bears
Bibliographic record
Abstract
The composition of the intestinal microbiota depends on gut physiology and diet. Ursidae possess a simple gastrointestinal system composed of a stomach, small intestine, and indistinct hindgut. This study determined the composition and stability of fecal microbiota of 3 captive polar bears by group-specific quantitative PCR and PCR-DGGE (denaturing gradient gel electrophoresis) using the 16S rRNA gene as target. Intestinal metabolic activity was determined by analysis of short-chain fatty acids in feces. For comparison, other Carnivora and mammals were included in this study. Total bacterial abundance was approximately log 8.5 DNA gene copies·(g feces)-1 in all 3 polar bears. Fecal polar bear microbiota was dominated by the facultative anaerobes Enterobacteriaceae and enterococci, and the Clostridium cluster I. The detection of the Clostridium perfringens α-toxin gene verified the presence of C. perfringens. Composition of the fecal bacterial population was stable on a genus level; according to results obtained by PCR-DGGE, dominant bacterial species fluctuated. The total short-chain fatty acid content of Carnivora and other mammals analysed was comparable; lactate was detected in feces of all carnivora but present only in trace amounts in other mammals. In comparison, the fecal microbiota and metabolic activity of captive polar bears mostly resembled the closely related grizzly and black bears.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".