Inferring white-tailed deer (Odocoileus virginianus) population dynamics from wildlife collisions in the City of Ottawa
Bibliographic record
Abstract
Concerns associated with growing white-tailed deer (Odocoileus virginianus) numbers in Ottawa, Ontario have motivated several studies related to the distribution and ecology of deer in the Ottawa-Carleton region. This project infers deer-population trends from deer-vehicle collisions in Ottawa, Ontario, and considers the influence of traffic volume on estimates of population dynamics from deer-vehicle collision data. Traffic volume and collision data for various road segments across suburban Ottawa were analyzed to answer questions related to the characteristics and spatial distribution of deer collisions and traffic volume in the city. Deer-vehicle collisions are increasing at a faster rate than traffic volume, suggesting that the deer population is increasing. The distribution of collisions supports the boundaries previously suggested for the location of one deerherd summer range, but not the other. Deer-collision numbers east and west of the Rideau River, a likely barrier to deer movement, were very similar, even though research and concern related to deer numbers has been concentrated west of the Rideau. More collisions occurred on 400-series highways than on other roads, suggesting that highways are a higher risk for deer collisions than other roads. The number of deer-vehicle collisions is much higher on recently constructed 400-series highways than on older 400-series highways, indicating that new highways represent high-risk areas for collisions. This research suggests that deer-vehicle collisions could be a very useful data source for inferring deer population dynamics of suburban deer, but it is imperative that significant factors affecting the number and distribution of collisions, such as category of road and traffic volume, are considered during any analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".