Monitoring digestibility of forages for herbivores: a new application for an old approach
Bibliographic record
Abstract
Ruminant populations are often limited by how well individuals are able to acquire nutrients for growth, maintenance, and reproduction. Nutrient supply to the animal is dictated by the concentration of nutrients in feeds and the efficiency of digesting those nutrients (i.e., digestibility). Many different methods have been used to measure digestibility of forages for wild herbivores, all of which rely on collecting rumen fluid from animals or incubation within animals. Animal-based methods can provide useful estimates, but the approach is limited by the expense of fistulated animals, wide variation in digestibility among animals, and contamination from endogenous and microbial sources that impairs the estimation of nutrient digestibility. We tested an in vitro method using a two-stage procedure using purified enzymes. The first stage, a 6 h acid–pepsin treatment, was followed by a combined 72 h amylase–cellulase or amylase–Viscozyme treatment. We then validated our estimates using in sacco and in vivo methods to digest samples of the same forages. In vitro estimates of dry matter (DM) digestibility were correlated with estimates of in sacco and in vivo DM digestibility (both P < 0.01). The in vitro procedure using Viscozyme (r2 = 0.77) was more precise than the in vitro procedure using cellulase (r2 = 0.59). Both procedures can be used to predict in sacco digestibility after correcting for the biases of each method. We used the in vitro method to measure digestibility of nitrogen (N; 0.07–0.95 g/g), which declined to zero as total N content declined below 0.03–0.06 g/g of DM. The in vitro method is well suited to monitoring forage quality over multiple years because it is reproducible, can be used with minimal investment by other laboratories without animal facilities, and can measure digestibility of individual nutrients such as N.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".