A comparison of elk‐vehicle collision patterns with demographic and abundance data in the <scp>Central Canadian Rocky Mountains</scp>
Bibliographic record
Abstract
Abstract Wildlife‐vehicle collisions are a widespread phenomenon that are influenced by species behavior, abundance, and road and landscape interactions. The mortality rate of different age and sex classes can buffer or exacerbate how the population responds to vehicle collisions. We evaluated the demographic‐specific patterns of elk‐vehicle collisions in the Central Canadian Rocky Mountains. More females and adults were involved in collisions, but when compared to the sex and age of the population, males and subadults were more prone to collisions in the fall. The fat marrow content (condition) of elk was greater for road‐ and rail‐kill than predator‐killed elk indicating that vehicle collisions are an additive source of mortality. As traffic volumes increased elk collisions decreased because elk declined over the study period. Evaluation of long‐term datasets can assist in designing mitigation that target the most vulnerable demographics of a population. For example, larger more open wildlife crossing structures have shown to be more suitable for vulnerable demographics such as female grizzly bears, male ungulates, and female ungulates traveling with young. When crossing structures are not practical, demographic‐specific information can inform outreach and awareness programs that strive to elicit a favorable response from motorists ultimately avoiding collisions with animals on roads.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".