The influence of adaptation of rumen microflora on in vitro digestion of different forages by sheep and red deer
Bibliographic record
Abstract
The rumen microflora ecosystem adapts to the diet consumed by the animal. We tested the extent to which this adaptation facilitates or retards the digestion of plant-based forages. Following adaptation of sheep (Ovis aries) and red deer (Cervus elaphus) to diets containing different mixtures of alfalfa, grass, and heather (a dwarf shrub), an in vitro digestion technique was used to compare the ability of the rumen microflora to digest the mixtures of substrates to which they were adapted with their ability to digest different mixtures of the same substrates. In vitro digestion of different substrates was slightly greater in rumen liquor derived from sheep than in that derived from red deer for each of the different substrates, but the effect was not significant. Digestibility in sheep was independent of how the feed was presented (diet of equal proportions of alfalfa, grass, and heather in each meal (D-EQ): mean in vitro digestibility = 37.3%; alfalfa, grass, and heather presented sequentially on different days (D-SEQ): mean in vitro digestibility = 37.7%, SE of differences = 1.30%, p > 0.05). However, in red deer there was a significant effect of method of diet presentation (D-EQ: mean in vitro digestibility = 36.9%; D-SEQ: mean in vitro digestibility = 34.2%, SE of differences = 1.30%, p < 0.05), digestibility being substantially lower for D-SEQ than for D-EQ. Overall, the results demonstrated that whilst there were no species-specific differences in overall digestion efficiency, dietary adaptation had an effect on substrate digestion efficiency, with rumen microbes adapted to high-quality diets digesting these more efficiently than low-quality diets.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".