Is winter diet quality related to body condition of white-tailed deer (<i>Odocoileus virginianus</i>)? An experiment using urine profiles
Bibliographic record
Abstract
During winter, boreal forest herbivores have access to only poor-quality forage. On Anticosti Island (Quebec, Canada), the ongoing reduction of balsam fir (Abies balsamea (L.) P. Mill.) owing to overbrowsing by white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) may force deer to include a higher proportion of white spruce (Picea glauca (Moench) Voss), a browse normally avoided, in their winter diet. We tested the hypotheses that (i) deer body condition during winter and (ii) the costs of detoxification of plant secondary metabolites in the winter diet could be estimated by monitoring the 3-methylhistidine / creatinine and glucuronic acid / creatinine ratios, respectively, in urine collected in snow from white-tailed deer fawns. Doubling the amount of white spruce in the winter diet of deer (from the current 20% under natural conditions to 40%) did not increase 3-methylhistidine / creatinine ratios but increased the glucuronic acid / creatinine ratio in urine, suggesting that a diet containing more spruce was more toxic. A weak positive relationship was observed between 3-methylhistidine and percent cumulative mass loss. There was no relationship between the 3-methylhistidine / creatinine ratio and the number of days left before death, as well as no relationship between the ratio of glucuronic acid / creatinine and percent cumulative mass loss. We conclude that the costs of detoxification of plant secondary metabolites in the winter diet of white-tailed deer in boreal forests could be monitored with glucuronic acid / creatinine ratios, but that 3-methylhistidine / creatinine ratios were weak indicators of deer body condition in winter.
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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 | 0.000 | 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 | 0.001 |
| 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".