Use of rumen–reticulum fill to examine nutrient transfer and factors influencing food intake in white-tailed deer (<i>Odocoileus</i> <i>virginianus</i>)
Bibliographic record
Abstract
Estimating relationships of gut fill in mammalian herbivores is useful to understanding digestive functions. Large animals might have more fluid in the gut to facilitate nutrient transfer between the gut lumen and the gut wall. Furthermore, relationships between concentrations of dietary refractory and indigestible fiber (CRIF) and gut fill might indicate whether chemostatic factors or physical distension of the gut affects food intake. We collected white-tailed deer (Odocoileus virginianus (Zimmermann, 1780); 122 males, 152 females) from three sites in central and south Texas that varied in diet quality as indexed by rumen–reticulum crude protein concentrations. Large animals did not have more fluid in their rumina–reticula than small animals because the scalar between body mass and wet mass of rumen–reticulum contents was not greater than the scalar estimated for dry mass of rumen–reticulum contents. We expected a positive or an inverse relationship when rates of forage comminution, digestion, and particle passage were high or low, respectively. At the site where deer had access to a high-quality pelleted diet, we detected a positive relationship between CRIF and dry mass. At sites with free-ranging deer and lower quality diets, relationships between CRIF and dry fill were inversely related. Food intake of deer was probably influenced by chemostatic factors at the site with a high-quality pelleted diet and by physical distension of the gut at the other two sites.
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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 | 0.001 |
| 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".