Effects of supplemental dietary tannins on the performance of white-tailed deer (<i>Odocoileus virginianus</i>)
Bibliographic record
Abstract
Tannins are natural and nutritionally significant components of the diets of browsing ungulates. In trials on supplemented pastures and in drylots, we estimated dry matter intake (DMI), weight gain, and urea N, potassium, cortisol and creatinine in urine of captive white-tailed deer fed pelleted diets that differed only in the respective quebracho tannin (QT) content. The low control, medium and high QT rations were 3.6, 63 and 152 g/kg DM respectively. There was no tannin-free pellet option. Trials were divided into winter pasture, restricted choice and spring growth. In winter pasture trial on pasture using QT, deer reduced QT intake relative to that expected under random foraging. This aversion was also apparent during the spring growth trial. While DMI in the winter pasture trial remained similar among treatments (p > 0.05), averaging 130 g/kg(0.75)/day, deer gained more weight (p < 0.05) when given a choice that included the high QT ration. During subsequent spring growth, DMI and weight gains generally exceeded those of the winter period. Unlike the winter pasture trial, weight gains in spring growth trial were higher (p < 0.05) in the low-control QT treatment. In the restricted choice trial, weight gain was again higher (p < 0.05) for deer fed a low-control QT diet. The urea N/creatinine ratio of deer fed the low-control QT diet (0.0357) was over three times that of deer fed the high QT diet (0.0107). Neither potassium/creatinine nor cortisol/creatinine ratios were affected by diet (p > 0.05). Collectively, these results suggest that although deer do not avoid tannins, and even ingested up to 5% under the choice options in these trials, the effect of tannins on deer performance may vary by season as well as by foraging opportunities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".