The gastrointestinal microbiota and its role in monogastric nutrition and health with an emphasis on pigs: Current understanding, possible modulations, and new technologies for ecological studies
Bibliographic record
Abstract
The gastrointestinal microbiota is an incompletely defined, dynamic community of several hundred species of primarily anaerobic bacteria. Species composition and bacterial numbers vary depending on animal age, the gastrointestinal location and a variety of nutritional and environmental factors. The microbiota positively and negatively impacts host physiology and performance in many important ways. This review will examine the establishment and composition of the normal microbiota; its beneficial and deleterious effects on the host; and methods by which to modify the microbiota. In addition, recent advances in methodology using the techniques of molecular biology to measure and describe the microbiota are discussed. Finally, recent results using the polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) technique to examine the microbiota of pigs at different ages, different intestinal sites, and after treatment with selected feed additives will be described. Key words: Microbiota, gastrointestinal tract, PCR-DGGE, animal nutrition/health, 16S rRNA
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".