Aerial Surveys Vs Hunting Statistics To Monitor Deer Density: The Example Of Anticosti Island, Québec, Canada
Bibliographic record
Abstract
Cervid densities have recently increased in many parts of North America and Europe. To design sustainable harvesting strategies, a good understanding of deer population dynamics and reliable estimates of population densities are required. This is especially true on Anticosti Island, Québec, Canada, where sport hunting is the main source of income, and where long-lasting impacts of white-tailed deer Odocoileus virginianus on the forest ecosystem have been reported due to high deer densities. We compared white-tailed deer densities estimated in 2001 on the basis of an extensive aerial survey of 512 plots, each 3.5 km long by 60 m wide, with indices based on hunting statistics in 24 hunting zones on the island. We found a positive correlation between the number of deer seen per hunter day and the density of deer estimated by the aerial survey, but this correlation was highly influenced by the four locations with the highest densities of deer. We detected no significant correlation between deer density estimated by the aerial survey within each hunting zone and the number of deer harvested per hunter day. Our results underline the need for comparative studies addressing the validity of density indices based on hunting statistics to monitor variations in cervid population numbers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".