Determinants of vigilance in a reintroduced population of Père David’s deer
Bibliographic record
Abstract
Abstract After being kept in captivity and isolated from natural predators for more than 1,200 years, Père David’s deer has been reintroduced in China and now occurs in a reserve where human activity is the only potential threat. Antipredator vigilance is an important component of survival for many prey animals in their natural habitat. Do deer still adjust vigilance as a function of risk after such a long period of relaxed predation pressure? Here, we examined vigilance levels in Père David’s deer groups as a function of group size, sex and level of human disturbance. The results showed that individual vigilance significantly decreased with group size in all-female groups but not in all-males or mixed-sex groups. In rutting season, males compete with one another and harass females, and we argue that vigilance is partly aimed at threatening males and that such vigilance increases with group size. This explains why overall vigilance did not vary with group size for males in general and for females in mixed-sex groups. Vigilance increased in more disturbed areas but in in male deer only. The results indicate that despite relaxed predation pressure over centuries, Père David’s deer can still adjust antipredator responses as a function of perceived risk. Such information may become useful in the rewilding programme now under way for this species in China.
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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 | 0.000 | 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".