Detection and Avoidance of Predators in White‐Tailed Deer (<i>Odocoileus virginianus</i>) and Mule Deer (<i>O. hemionus</i>)
Bibliographic record
Abstract
In this paper, we investigate the relationship between early detection of predators and predator avoidance in white‐tailed deer (Odocoileus virginianus) and mule deer (O. hemionus), two closely related species that differ in their habitat preferences and in their anti‐predator behavior. We used observations of coyotes (Canis latrans) hunting deer to test whether the distance at which white‐tails and mule deer alerted to coyotes was related to their vulnerability to predation. Coyote encounters with both species were more likely to escalate when deer alerted at shorter distances. However, coyote encounters with mule deer progressed further than encounters with white‐tails that alerted at the same distance, and this was not due to species differences in group size or habitat. We then conducted an experiment in which a person approached groups of deer to compare the detection abilities and the form of alert response for white‐tails and mule deer, and for age groups within each species. Mule deer alerted to the approacher at longer distances than white‐tails, even after controlling for variables that were potentially confounding. Adult females of both species alerted sooner than conspecific juveniles. Mule deer almost always looked directly at the approacher as their initial response, whereas white‐tails were more likely to flee or to look in another direction with no indication that they pinpointed the approacher during the trial. Mule deer may have evolved the ability to detect predators earlier than white‐tails as an adaptation to their more open habitats, or because they need more time to coordinate subsequent anti‐predator defenses.
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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".