Understanding urban white-tailed deer (Odocoileus virginianus) movement and related social and ecological considerations for management
Bibliographic record
Abstract
White-tailed deer (WTD) (Odocoileus virginianus) were studied within the Greater Winnipeg Area (GWA) to investigate urban deer home range size, habitat use, and seasonal movement patterns. A comparative analysis was also completed in Riding Mountain National Park (RMNP) in order to assess the similarities and differences between urban and rural deer spatial and temporal movement patterns. The study revealed differences in the spatial land use patterns of these two cohorts with substantially smaller urban WTD monthly and seasonal home range sizes than in RMNP. Building on the findings derived from the animal-borne locational data, an investigation into the human social dynamics associated with the urban deer herd indicated that human behavior heavily influences urban deer movement. Using a critical case study approach, the research investigated the wildlife value orientations and the emotional dispositions associated with the human behavior of intentionally supplying artificial food sources for deer. The spatial and temporal occurrences of urban deer-vehicle collisions (DVCs) and the factors associated with high risk DVC roadways were not random, and human social behavior is correlated to the frequency and location of DVC occurrences in the GWA. This research identifies management strategies to successfully mitigate human-wildlife conflict and the associated human-human conflict within the GWA, as well as the need for, and challenges associated with, an integrated approach to urban wildlife management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".