Comparison of gut fill in sheep (<i>Ovis</i> <i>aries</i>) measured by intake, digestibility, and digesta retention compared with measurements at harvest
Bibliographic record
Abstract
Gut capacity is an important factor in digestive physiology and is often measured as dry matter fill (DMF) following dissection, which prevents repeated measures in the same animal. It was proposed to calculate DMF from food intake, digestibility, and gut mean retention time (MRT), but empirical tests of this are few. We calculated DMF from intake, digestibility, and the MRT of small-particle (1 mm) and large-particle (20 mm) markers in 20 sheep (Ovis aries L., 1758) fed at different intake levels and compared results with DMF at dissection at the end of the feeding trial. MRT for smaller particles was significantly shorter than for larger particles (34.4 ± 6.1 vs. 42.5 ± 7.6 h, respectively). Correspondingly, DMF calculated from smaller particles (0.98 ± 0.27 kg) was significantly lower than DMF calculated from larger particles (1.20 ± 0.30 kg). The latter was not significantly different from DMF measured at dissection (1.18 ± 0.34 kg). These results suggest that DMF can be estimated from measures of digestive physiology. The choice of particle marker to determine MRT is crucial for the accuracy of the proxy. In ruminants, where small particles are consistently eliminated faster than larger particles, considerations of marker particle size are particularly important.
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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 | 0.001 | 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 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".