Summer Roadside use by White-Tailed Deer and Mule Deer in the Rocky Mountains, Alberta
Bibliographic record
Abstract
Deer-vehicle collisions are on the rise in North America, requiring a better understanding of road use patterns by deer. We examined summer use of roadside areas by White-tailed Deer (Odocoileus virginianus) and Mule Deer (Odocoileus hemionus) in the central Rocky Mountains, Alberta. Deer surveys were conducted along the main highway at dawn and dusk for 6 summers. We observed more White-tailed Deer than Mule Deer along the highway during the study. White-tailed Deer were also involved in collisions with vehicles more often than Mule Deer, and may pose a higher risk for collisions because they tend to flee when approached. Time of day did not affect Mule Deer sightings during the study period, but White-tailed Deer were observed more frequently in the morning than evening. Both species were observed more frequently in May than other months. While little association was observed between deer species, large-scale spatial segregation along the highway did not occur. Our data suggest that drivers were likely to encounter deer in single-species pairs, and based on deer roadside use, we suggest that the potential for deer-vehicle collisions was highest in May, close to dawn, and along the northern sections of the highway. Deer-vehicle collision data indicated that the predominant locations of collisions reflected the spatial patterns of roadside usage by deer, but temporal patterns of collisions are also affected by visibility and traffic patterns. Collision-mitigation strategies incorporating deer behavior and driver-education are discussed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".