Contribution of dogs to white‐tailed deer hunting success
Bibliographic record
Abstract
Abstract Dogs (Canis familiaris) are used in hunting white‐tailed deer (Odocoileus virginianus) in 10 North American jurisdictions. Although the practice is longstanding and controversial, the effects of dogs on the outcome of the hunt have rarely been studied. We evaluated the influence of dogs on recreational hunting of white‐tailed deer based on long‐term data from southeastern Ontario, Canada. Over 25 years, annual surveys of hunters were used to collect data on hunting effort and deer harvest from approximately 85 camps, roughly half of which had dogs. We investigated the relationship between harvest and 3 treatments (i.e., 0 dogs, 1 dog, and ≥2 dogs in camp), interactions with weather and deer density, and effects of neighboring camps. Dogs enhanced hunter success. We found no difference in deer encounter rates but, per unit effort, camps with ≥2 dogs harvested 0.013 (26%) more deer per hunter‐day, missed 0.010 (23%) more deer per hunter‐day, and wounded 0.002 (40%) more deer per hunter‐day than camps without dogs. Conversely, camps without dogs saw, without shooting at, 0.033 (23%) more deer per hunter‐day than camps with ≥2 dogs. These results are consistent with the idea that hunters with dogs are less selective. Hunters with dogs harvested more fawns per unit effort, but we found no difference in the harvest rate of older female deer. More precipitation, greater wind speed, lower temperatures and greater deer density improved harvest success but had no differential effect among dog treatments. Hunter success at camps with ≥2 dogs was less when neighboring camps also had ≥2 dogs. Because antlerless deer quotas are the principal means to control populations, increasing use of hunting dogs is unlikely to have substantial effects in managing overabundant deer. © 2012 The Wildlife Society.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".