Urinary metabolites as an index of body condition in wintering white-tailed deer Odocoileus virginianus
Bibliographic record
Abstract
We tested whether snow-urine ratios to creatinine of urea nitrogen (U:C), potassium (K:C), allantoin (A:C) and 3-methylhistidine (M:C) could be used to determine when to initiate an emergency feeding program in white-tailed deer Odocoileus virginianus. Food distribution to 11 experimental adult deer was gradually reduced over 64 days to simulate the conditions occurring for wild deer during winter. At the end of the partial fasting period, experimental deer had lost 19% of body mass on average. The animals were then fed ad libitum during a 13-day recovery period. A control group of four deer was fed ad libitum during the entire study. Control deer lost 6% of body mass during the experiment. Results for U:C and K:C ratios suggest that they were unreliable as indicators of physical condition of white-tailed deer during winter, at least within the physiological range and sample size considered in this study. A:C ratios showed fluctuations that were congruent with current knowledge of fasting physiology. A:C ratios of experimental deer relative to control deer, however, increased significantly only after 64 days of partial fasting, when animals had lost 19% of body mass. At that time it may already be too late to launch an effective feeding program. K:C and A:C ratios also increased during the recovery period, illustrating the potential difficulty of determining whether such an increase results from starvation or from resumed food intake. Concentrations of 3-methyl-histidine in the snow remained too low to be detected, due to dilution. We conclude that, under the limits of this study, none of the creatinine ratios represents an accurate index of body condition to determine when to initiate an emergency feeding program.
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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 | 0.001 | 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 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".