Behavioral responses of white-tailed deer subjected to lethal management
Bibliographic record
Abstract
Currently, the most effective and cost-efficient mechanism for controlling overabundant white-tailed deer ( Odocoileus virginianus (Zimmermann, 1780)) is lethal removal, most commonly controlled hunting and sharpshooting. Deer subjected to such efforts may behave differently during removal and remaining deer may alter behaviors, potentially limiting future efficacy of removal efforts. Our objectives were to quantify changes in deer distribution in response to controlled hunting and sharpshooting. We immobilized two sample populations of 20 deer (one enclosed and one free-ranging) in central New Jersey, USA, and fitted them with global positioning system collars. The free-ranging herd experienced 11 days of controlled hunting, reducing density from 78 to 27 deer/km2. We subjected the enclosed herd to a 7 day sharpshoot, reducing density from 83 to 7 deer/km2. Hunted deer increased mean home ranges during removal, while deer exposed to sharpshooting did not. Collared doe–doe home-range overlap increased postsharpshoot, suggesting increased social interaction. Behaviors of hunted deer were directly affected by the human threat, while behavioral changes of deer exposed to sharpshooting were linked to population reduction. In the absence of an intact matrilineal social group, unrelated does will seek each other out in what appears to be an inherent need to be social.
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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".