Model development: establishing pigs with homogenous microbial profile in the hind gut
Bibliographic record
Abstract
The considerable animal-to-animal variation in microbial profiles is a challenge in elucidating the role of gut microbiota in host metabolism. The main purpose of this study was, therefore, to develop a pig model with reduced animal-to-animal variation in gut microbial profile. Twelve piglets from four sows were reared conventionally and 12 piglets from four sows were reared artificially in high efficiency particulate air (HEPA) filtered isolators. All isolator-reared piglets were given an artificial colostrum formula containing the combined fecal material from all eight sows. All piglets were killed at 21 d of age and intestinal contents subjected to 16s rRNA gene-based terminal restriction fragment length polymorphism (T-RFLP) profiling. Resulting T-RFLP profiles clustered into two distinct groups representing the two treatment groups. Furthermore, Bray–Curtis dissimilarity distance values and Dice similarity indices showed reduced beta diversity in isolator-reared pigs indicating animal-to-animal variation was reduced in isolator-reared compared to conventional piglets. However, surprisingly, increased alpha diversity was observed in isolator-reared piglets compared with conventional piglets. In conclusion, the study demonstrated that rearing of piglets under conditions of controlled environment reduced animal-to-animal variation in the hindgut microbiota while paradoxically increasing within animal microbial diversity. Isolator rearing may be useful as a model to improve detection of treatment effects on gut microbiota.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".