Daytime declines in rumen–reticulum fill of male white-tailed deer (<i>Odocoileus virginianus</i>) from south Texas
Bibliographic record
Abstract
Understanding rumen–reticulum fill dynamics gives us insight into how environmental conditions influence animal behaviors that affect when animals access forage. We examined whether there were daytime declines in rumen–reticulum fill in response to high ambient temperatures in free-ranging white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) on a diurnal scale. We hypothesized that to reduce heat-producing digestive processes in the heat of the day, rumen–reticulum fill and digestion in the rumen–reticulum would decline throughout the day. We collected the rumina and reticula of adult males and females across 30 h in two autumns in south Texas, USA. We also measured dry digesta mass, organ mass, and crude protein and acid detergent fiber concentrations of rumen–reticulum contents. In males, dry digesta mass declined throughout the day and crude protein concentrations were greatest when wet digesta and organ mass were the largest. Additionally, declines in dry digesta and wet digesta mass coincided with declines in crude protein concentrations and organ mass. Females did not display declines in rumen–reticulum fill (wet mass). Females were probably less vulnerable to heat than males because of their smaller body size. Male white-tailed deer foraged primarily during the cooler nights to reduce foraging and digestion during the heat of the day.
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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 | 0.000 | 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".