Rumen physiology constrains diet niche: linking digestive physiology and food selection across wild ruminant species
Bibliographic record
Abstract
We propose a hypothesis for digestive constraints on the browsing and grazing options available to ruminants: that the diet-niche range (maximum and minimum grass intake) of a species is dependent upon its predisposition to stratified rumen contents, based on observations that this characteristic is a critical step towards enhanced fibre digestion and greater fluid throughput. We compare a physiological (heterogeneity of ingesta fluid content) and an anatomical (the intraruminal papillation pattern) measure with dietary evidence for a range of African and temperate species. Both measures are strongly related to the mean percentage of grass in species’ natural diets, as well as to the maximum and minimum levels of grass intake, respectively. The nature of these effects implies a stratification-level threshold, below which a species will not use a grass-based diet, but above which grass consumption can increase exponentially. However, above this threshold, a minimum percentage of grass in the diet is a prerequisite for optimal performance. We argue that this second constraint is crucial, as it depicts how a greater fluid throughput reduces potential for detoxification of plant secondary compounds, and therefore limits the maximum amount of browse a stratifying species will consume.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".