Impacts of human hunting on spatial behavior of white-tailed deer (<i>Odocoileus</i> <i>virginianus</i>)
Bibliographic record
Abstract
Predators can influence populations through top-down effects, but most large predators have been extirpated from the range of white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)). Hunters have filled this predatory role, but also can indirectly influence prey species. Indirect behavioral responses can include altered resource selection, space use, or movement. Herein, we developed a controlled study that contained both temporal and spatial risk levels to assess how deer behavior changes relative to temporal periods of risk. Total distance travelled and microrange area over 2-day periods were used to determine the general effects of hunting season on deer spatial behavior. Generally, distance travelled, microrange area, and exploratory behavior decreased during the course of the study, with the greatest decrease occurring during the active 16-day hunting period. Despite potential risk and disturbance from hunters, deer maintained site fidelity to previously established ranges and did not expand microrange areas. These data indicate that deer recognize threats from humans on the landscape and adapt behavioral strategies by minimizing movement and exhibiting high residency times in well-established ranges, factors known to influence harvest susceptibility. This information can be used to assess potential impacts from hunting for management purposes, but also to test the adaptive ability of animals to risk.
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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 | 0.000 | 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 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".