Contributions of digestive plasticity to the ability of white-tailed deer to cope with a low-quality diet
Bibliographic record
Abstract
Many herbivores exhibit phenotypic variations of their digestive system in response to changes in quality of food resources. This digestive plasticity is considered an adaptive trait for individuals to help them cope with variation in food resources and to fulfill nutritional needs. We investigated whether digestive phenotypic variations could contribute to sustain the population of introduced white-tailed deer ( Odocoileus virginianus ) on Anticosti Island (Québec, Canada) facing a winter diet of low-quality forage. We compared digestive morphology and in vitro digestibility of winter forage to that of deer from the original mainland population. Deer on Anticosti Island had a higher ruminal volume and digesta load (43% and 62%, respectively), greater absorption surface of the ruminal papillae, and greater relative mass of all forestomachs than deer from the mainland. Woody forage digestibility was similar between the 2 populations, even though faster kinetic digestion may occur for deer on Anticosti Island. Digestive plasticity appears to play a central role in sustaining high deer densities facing harsh forage conditions on Anticosti Island. Comparisons of digestive morphology and digestibility between populations that have access to forage of variable quality contribute to our understanding of the digestive response and the role of digestive plasticity for individuals facing a decline in diet quality.
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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 | 0.000 |
| 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) | 0.001 | 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".