Dead mammal walking: A month‐long march by a bison (<i>Bison bison</i>) after an ungulate–vehicle collision
Bibliographic record
Abstract
Abstract Globally, ungulate–vehicle collisions (UVCs) are a major human safety concern and may also represent a significant source of mortality for some ungulate populations. However, records of UVC based on counts of roadside carcasses or reports by drivers involved in these incidents are assuredly underestimated because not all ungulates struck die immediately or at the roadside or are reported by drivers or authorities. Here, we provide an observation and analysis of the movements of a GPS‐collared bison (Bison bison) that was involved in a UVC on the Alaska Highway and died in a thick boreal forest 29 days later. During that time, she moved 49.7 km from where she was hit. Her daily movement rate (in kilometers per hour) and daily net displacement (in kilometers per day) were significantly greater in the 29‐day period before she was struck compared with 29 days afterward. This vivid example illustrates that individuals injured in a UVC can die several weeks later and at a considerable distance from where they were initially struck. Moreover, when they eventually die, it may be where their carcass would not be found or associated with a UVC. Bison are the largest land mammal in North America and perhaps more robust to some lower impact UVC than smaller bodied species. Even so, if not for the GPS collar on this bison, we would have never known the fate of this individual, and the carcass likely never found. Taken together, the movements and final resting place of this bison illuminate how estimates of mortality as a result of UVC can be underestimated when the animal does not die immediately and in a location where it can be found. Given our data, we further urge managers to consider roadside counts of animals killed in UVC as a minimum estimate when considering options for mitigation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".